From b5d2bfad24dc94af14f8ba7f093cdf5786261ca0 Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Mon, 22 Jan 2018 17:05:31 -0500 Subject: [PATCH 01/20] tf records generation from labels.npz --- examples/utils/tf_records_generation.py | 185 ++++++++++++++++++++++++ 1 file changed, 185 insertions(+) create mode 100644 examples/utils/tf_records_generation.py diff --git a/examples/utils/tf_records_generation.py b/examples/utils/tf_records_generation.py new file mode 100644 index 0000000..1880b6d --- /dev/null +++ b/examples/utils/tf_records_generation.py @@ -0,0 +1,185 @@ +""" +This code was modified on top of Google tensorflow +(https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md) + +To use this code with Label Maker and Tensor Flow object detection API, this code work similar to `label-maker package`. +To create a correct training data set for Tensor Flow Object Detecrtion, we recommend you do: +1. After running `label-maker images`, do `git clone https://github.com/tensorflow/models.git` +2. Install TensorFlow object detection by following this: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md +3. From your Label Maker, copy `tiles` folder, this code `tf_records_generation.py` and `labels.py` to Tensorflow object detecrtion directory +4. From directory `tensorflow/models/research/` simply runs: +python3 tf_records_generation.py --label_input=labels.npz \ + --train_rd_path=data/train_buildings.record \ + --test_rd_path=data/test_buildings.record +""" + +import os +import io +import numpy as np +from os import makedirs, path as op +import shutil + +import pandas as pd +import tensorflow as tf + +from PIL import Image +from utils import dataset_util +from collections import namedtuple + + +flags = tf.app.flags +flags.DEFINE_string('label_input', '', 'Path to the labels.npz input') +flags.DEFINE_string('train_rd_path', '', 'Path to output TFRecord') +flags.DEFINE_string('test_rd_path', '', 'Path to output TFRecord') +FLAGS = flags.FLAGS + + +# TO-DO replace this with label map +def class_text_to_int(row_label): + if row_label == 'building': + return 1 + return None + + +def split(df, group): + data = namedtuple('data', ['filename', 'object']) + gb = df.groupby(group) + return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)] + +def create_tf_example(group, path): + """Creates a tf.Example proto from sample buillding image tile. + +Args: + encoded_building_image_data: The jpg encoded data of the building image. + +Returns: + example: The created tf.Example. +""" + with tf.gfile.GFile(op.join(path, '{}'.format(group.filename)), 'rb') as fid: + encoded_jpg = fid.read() + encoded_jpg_io = io.BytesIO(encoded_jpg) + image = Image.open(encoded_jpg_io) + width, height = image.size + filename = group.filename.encode('utf8') + image_format = b'jpg' + xmins = [] + xmaxs = [] + ymins = [] + ymaxs = [] + classes_text = [] + classes = [] + + for _, row in group.object.iterrows(): + xmins.append(row['xmin'] / width) + xmaxs.append(row['xmax'] / width) + ymins.append(row['ymin'] / height) + ymaxs.append(row['ymax'] / height) + classes_text.append(row['class'].encode('utf8')) + classes.append(class_text_to_int(row['class'])) + + tf_example = tf.train.Example(features=tf.train.Features(feature={ + 'image/height': dataset_util.int64_feature(height), + 'image/width': dataset_util.int64_feature(width), + 'image/filename': dataset_util.bytes_feature(filename), + 'image/source_id': dataset_util.bytes_feature(filename), + 'image/encoded': dataset_util.bytes_feature(encoded_jpg), + 'image/format': dataset_util.bytes_feature(image_format), + 'image/object/bbox/xmin': dataset_util.float_list_feature(xmins), + 'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs), + 'image/object/bbox/ymin': dataset_util.float_list_feature(ymins), + 'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs), + 'image/object/class/text': dataset_util.bytes_list_feature(classes_text), + 'image/object/class/label': dataset_util.int64_list_feature(classes), + })) + return tf_example + + +def main(_): + labels = np.load(op.join(os.getcwd(), FLAGS.label_input)) + tile_names = [tile for tile in labels.files] + tile_names.sort() + tiles = np.array(tile_names) + + tf_tiles_info = [] + + for tile in tiles: + bboxes = labels[tile].tolist() + width = 256 + height = 256 + if len(bboxes) != 0: + for bbox in bboxes: + if bbox[4] == 1: + cl_str = "building" + if bbox[0] < 0: + bbox[0] = 0 + if bbox[1] < 0: + bbox[1] = 0 + if bbox[2] > 256: + bbox[2] = 256 + if bbox[3] > 256: + bbox[3] = 256 + y = ["{}.jpg".format(tile), width, height, cl_str, bbox[0], bbox[1], bbox[2], bbox[3]] + tf_tiles_info.append(y) + #train_len = 0.8 + split_index = int(len(tf_tiles_info) *0.8 ) + column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] + df = pd.DataFrame(tf_tiles_info, columns=column_name) + #shuffle the dataframe + df = df.sample(frac=1) + train_df = df[:split_index] + test_df = df[split_index:] + # train_df.to_csv("train_df.csv") + # test_df.to_csv("test_df.csv") + + tiles_dir = op.join(os.getcwd(), 'tiles') + train_dir = op.join(os.getcwd(), 'images', 'train') + test_dir = op.join(os.getcwd(), 'images', 'test') + + if not op.isdir(train_dir): + makedirs(train_dir) + if not op.isdir(test_dir): + makedirs(test_dir) + + for tile in train_df['filename']: + tile_dir = op.join(tiles_dir, tile) + shutil.copy(tile_dir, train_dir) + + for tile in test_df['filename']: + tile_dir = op.join(tiles_dir, tile) + shutil.copy(tile_dir, test_dir) + ### for train + writer = tf.python_io.TFRecordWriter(FLAGS.train_rd_path) + grouped = split(train_df, 'filename') + + for group in grouped: + tf_example = create_tf_example(group, train_dir) + writer.write(tf_example.SerializeToString()) + writer.close() + output_train= op.join(os.getcwd(),FLAGS.train_rd_path) + print('Successfully created the TFRecords: {}'.format(output_train)) + + ### for test + writer = tf.python_io.TFRecordWriter(FLAGS.test_rd_path) + grouped = split(test_df, 'filename') + for group in grouped: + tf_example = create_tf_example(group, test_dir) + writer.write(tf_example.SerializeToString()) + + writer.close() + output_test = op.join(os.getcwd(),FLAGS.test_rd_path) + print('Successfully created the TFRecords: {}'.format(output_test)) + +def _score_converter_fn_with_logit_scale(tf_score_converter_fn, logit_scale): + def score_converter_fn(logits): + cr = logit_scale + cr = tf.constant([[cr]],tf.float32) + print(logit_scale) + print(logits) + scaled_logits = tf.divide(logits, cr, name='scale_logits') #change logit_scale + return tf_score_converter_fn(scaled_logits, name='convert_scores') + score_converter_fn.__name__ = '%s_with_logit_scale' % (tf_score_converter_fn.__name__) + return score_converter_fn + + +if __name__ == '__main__': + tf.app.run() From dad5d868a9e346a9780a588a76e5ea89880517bd Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Tue, 23 Jan 2018 15:22:59 -0500 Subject: [PATCH 02/20] header text edit --- examples/utils/tf_records_generation.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/utils/tf_records_generation.py b/examples/utils/tf_records_generation.py index 1880b6d..eb9c012 100644 --- a/examples/utils/tf_records_generation.py +++ b/examples/utils/tf_records_generation.py @@ -6,7 +6,7 @@ To create a correct training data set for Tensor Flow Object Detecrtion, we recommend you do: 1. After running `label-maker images`, do `git clone https://github.com/tensorflow/models.git` 2. Install TensorFlow object detection by following this: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md -3. From your Label Maker, copy `tiles` folder, this code `tf_records_generation.py` and `labels.py` to Tensorflow object detecrtion directory +3. From your Label Maker, copy `tiles` folder, this code `tf_records_generation.py` and `labels.npz` to Tensorflow object detecrtion directory `tensorflow/models/research/` 4. From directory `tensorflow/models/research/` simply runs: python3 tf_records_generation.py --label_input=labels.npz \ --train_rd_path=data/train_buildings.record \ From 87032e936564b0f7a899bb172ae8abadf644feee Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Tue, 23 Jan 2018 16:52:40 -0500 Subject: [PATCH 03/20] give some tile NO. info for train and test --- examples/utils/tf_records_generation.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/examples/utils/tf_records_generation.py b/examples/utils/tf_records_generation.py index eb9c012..9867e62 100644 --- a/examples/utils/tf_records_generation.py +++ b/examples/utils/tf_records_generation.py @@ -128,6 +128,8 @@ def main(_): df = df.sample(frac=1) train_df = df[:split_index] test_df = df[split_index:] + print("You have {} training tiles and {} test tiles ready".format( + len(set(train_df['filename'])), len(set(test_df['filename'])))) # train_df.to_csv("train_df.csv") # test_df.to_csv("test_df.csv") From d745b8d053946612c5ecbf393bf8f9f0b222d8ef Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Wed, 24 Jan 2018 10:46:13 -0500 Subject: [PATCH 04/20] prediction code for tf object detection --- examples/utils/tf_od_predict.py | 115 ++++++++++++++++++++++++++++++++ 1 file changed, 115 insertions(+) create mode 100644 examples/utils/tf_od_predict.py diff --git a/examples/utils/tf_od_predict.py b/examples/utils/tf_od_predict.py new file mode 100644 index 0000000..e33e6f1 --- /dev/null +++ b/examples/utils/tf_od_predict.py @@ -0,0 +1,115 @@ +""" +This is adepted from Tensorflow (https://github.com/tensorflow/models/tree/master/research/object_detection); +Save this code under the diractory of `models/research/object_detection/` + +To use, run: +python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ + --path_to_label=data/building_od.pbtxt \ + --test_image_path=test_images +""" + +import os +from os import makedirs, path as op +import sys +import glob +import six.moves.urllib as urllib +import tensorflow as tf +import tarfile + +from io import StringIO +import zipfile +import numpy as np +from collections import defaultdict +from matplotlib import pyplot as plt +from PIL import ImageDraw, Image + +sys.path.append("..") + +from utils import label_map_util +from utils import visualization_utils as vis_util + + + +flags = tf.app.flags +flags.DEFINE_string('model_name', '', 'Path to frozen detection graph') +flags.DEFINE_string('path_to_label', '', 'Path to label file') +flags.DEFINE_string('test_image_path', '', 'Path to test imgs and output diractory') +FLAGS = flags.FLAGS + +def load_image_into_numpy_array(image): + (im_width, im_height) = image.size + return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8) + +def tf_od_pred(): + with detection_graph.as_default(): + with tf.Session(graph=detection_graph) as sess: + # Definite input and output Tensors for detection_graph + image_tensor = detection_graph.get_tensor_by_name('image_tensor:0') + # Each box represents a part of the image where a particular object was detected. + detection_boxes = detection_graph.get_tensor_by_name('detection_boxes:0') + # Each score represent how level of confidence for each of the objects. + # Score is shown on the result image, together with the class label. + detection_scores = detection_graph.get_tensor_by_name('detection_scores:0') + detection_classes = detection_graph.get_tensor_by_name('detection_classes:0') + num_detections = detection_graph.get_tensor_by_name('num_detections:0') + for image_path in test_imgs: + image = Image.open(image_path) + image_np = load_image_into_numpy_array(image) + # the array based representation of the image will be used later in order to prepare the + # result image with boxes and labels on it. + # Expand dimensions since the model expects images to have shape: [1, None, None, 3] + image_np_expanded = np.expand_dims(image_np, axis=0) + # Actual detection. + (boxes, scores, classes, num) = sess.run( + [detection_boxes, detection_scores, detection_classes, num_detections], + feed_dict={image_tensor: image_np_expanded}) + # draw_bounding_box_on_image(image, boxes, ) + # Visualization of the results of a detection. + vis_image = vis_util.visualize_boxes_and_labels_on_image_array( + image_np, + np.squeeze(boxes), + np.squeeze(classes).astype(np.int32), + np.squeeze(scores), + category_index, + use_normalized_coordinates=True, + line_thickness=1) + print("{} boxes in {} image tile!".format(len(boxes), image_path)) + image_pil = Image.fromarray(np.uint8(vis_image)).convert('RGB') + with tf.gfile.Open(image_path, 'w') as fid: + image_pil.save(fid, 'PNG') + + + +if __name__ =='__main__': + # load your own trained model inference graph. This inference graph was generated from + # export_inference_graph.py under model directory, see `models/research/object_detection/` + model_name = op.join(os.getcwd(), FLAGS.model_name) + # Path to frozen detection graph. + path_to_ckpt = op.join(model_name, 'frozen_inference_graph.pb') + # Path to the label file + path_to_label = op.join(os.getcwd(), FLAGS.path_to_label) + #only train on buildings + num_classes = 1 + #Directory to test images path + test_image_path = op.join(os.getcwd(), FLAGS.test_image_path) + test_imgs = glob.glob(test_image_path + "/*.jpg") + + ############ + #Load the frozen tensorflow model into memory + ############# + + detection_graph = tf.Graph() + with detection_graph.as_default(): + od_graph_def = tf.GraphDef() + with tf.gfile.GFile(path_to_ckpt, 'rb') as fid: + serialized_graph = fid.read() + od_graph_def.ParseFromString(serialized_graph) + tf.import_graph_def(od_graph_def, name='') + + ############ + #Load the frozen tensorflow model into memory + ############# + label_map = label_map_util.load_labelmap(path_to_label) + categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=num_classes, use_display_name=True) + category_index = label_map_util.create_category_index(categories) + tf_od_pred() From 39c2e52dc093208cb1c7a9e7e774c79a36b96ccc Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Wed, 24 Jan 2018 10:50:17 -0500 Subject: [PATCH 05/20] minor text edit --- examples/utils/tf_od_predict.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/utils/tf_od_predict.py b/examples/utils/tf_od_predict.py index e33e6f1..3b602ec 100644 --- a/examples/utils/tf_od_predict.py +++ b/examples/utils/tf_od_predict.py @@ -95,7 +95,7 @@ def tf_od_pred(): test_imgs = glob.glob(test_image_path + "/*.jpg") ############ - #Load the frozen tensorflow model into memory + #Load the frozen tensorflow model ############# detection_graph = tf.Graph() @@ -107,7 +107,7 @@ def tf_od_pred(): tf.import_graph_def(od_graph_def, name='') ############ - #Load the frozen tensorflow model into memory + #Load the label file ############# label_map = label_map_util.load_labelmap(path_to_label) categories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=num_classes, use_display_name=True) From 0be68665eeb2ce7d77077a86232913b924897051 Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Wed, 24 Jan 2018 14:52:16 -0500 Subject: [PATCH 06/20] add a walkthrough to tf object detection --- ...walkthrough-tensorflow-object-detection.md | 133 ++++++++++++++++++ 1 file changed, 133 insertions(+) create mode 100644 examples/walkthrough-tensorflow-object-detection.md diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md new file mode 100644 index 0000000..a53a2eb --- /dev/null +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -0,0 +1,133 @@ +# Example Use: A building object detection with Tensor Flow + + + +# Download Training dataset +Vietnam has good image through Mapbox Satellite Imagery, so we are going to use the same configure JSON file we used for [another walkthrough](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md) +- Install Label Maker by: `pip install label_maker` . +- Create vietnam_tf.json for `object-detection` like shown in following json file. +```json +{ + "country": "vietnam", + "bounding_box": [105.42,20.75,106.41,21.53], + "zoom": 15, + "classes": [ + { "name": "Buildings", "filter": ["has", "building"] } + ], + "imagery": "http://a.tiles.mapbox.com/v4/mapbox.satellite/{z}/{x}/{y}.jpg?access_token=ACCESS_TOKEN", + "background_ratio": 1, + "ml_type": "object-detection" +} +``` +If you don't know know what is `country`, `bounding_box`, `zoom`, `classes`, `imagery`, `background_ratio` and `ml_type`, please check other two walkthrough examples we provided: [image classification with AWS and Keras](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-aws.md) and [image classification with MXNet and SageMaker](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md). + +# Training dataset generation +We'll follow the [CLI commands from the README](https://github.com/developmentseed/label-maker#command-line-use) but use a separate folder to keep our project well-managed. + +```shell +$ label-maker download --dest vietnam_building_tf --config vietnam_tf.json +$ label-maker labels --dest vietnam_building_tf --config vietnam_tf.json +``` +These commands will download and retile the OpenStreetMap QA tiles, label maker will find buildings from OpenSreetMap and draw bounding box/boxes, you could visualize the tiles in your `vietnam_building_tf/labels` folder that label maker creates. +The label tiles will be save as an numpy `labels.npz` that you will spot in the `vietnam_building_tf`folder. + +![26014-14421-15](https://user-images.githubusercontent.com/14057932/35344642-a130760e-00fb-11e8-85ea-628e00c13dd5.png) ![26014-14425-15](https://user-images.githubusercontent.com/14057932/35344651-a75e74fe-00fb-11e8-95d3-3685c6fa170f.png) + +You could preview how the building bounding boxes are drawn on top of the RGB image tiles by: + +```shell +$ label-maker preview -n 10 --dest vietnam_building_tf --config vietnam_tf.json +``` +You will see there are ten image tiles download in the foler `vietnam_building_tf/examples/Buildings/` + +![25989-14427-15](https://user-images.githubusercontent.com/14057932/35346218-a375d342-00ff-11e8-9ee1-8ff5c4396290.jpg) ![25991-14439-15](https://user-images.githubusercontent.com/14057932/35346236-b2c3e3ac-00ff-11e8-9ad3-250a756e852f.jpg) + +The building bounding boxes overlaid with image tiles will be look like above images. +You could tell from the above image tiles that a lot of buildings in Vietnam have been mapped and without bounding boxes yet, which would impact on our model prediction accuracy. If you’d like to help improve the labelling accuracy, [start mapping on OpenStreetMap](https://www.openstreetmap.org/#map=10/20.9755/105.4118). +Download all the image tiles that contain buildings by running +```shell +$ label-maker images --dest vietnam_building_tf --config vietnam_tf.json +``` +You will have 385 image tiles in your folder `vietnam_building_tf/tiles`You don't need to run `label-maker package` for this TensorFlow Object Detection task. + +Now, you are ready to set up Tensor Flow object detection API + +# Setup TensorFlow Object Detection API +### Install TensorFlow object detection: +- `git clone https://github.com/tensorflow/models.git`; +- Install TensorFlow Object Detection API by following strictly [this readme](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). You need to have everything installed, and after `python object_detection/builders/model_builder_test.py` past you will be ready to go to next step. + +### Create tensorflow TFRecords training. +Tensorflow API supports a variety of file formats. TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have a python code [here on our github repo](TODO...After this branch merge) for you to create TFRecords from `labels.npz` that Label Maker created under `label-maker labels` command. + +Follow these steps to create TFRecards +- Create the `tf_records_generation.py` under your Tensorflow object detection directory from [our github repo](), which is `models/research/object_detection/`. +- Copy and paste your `labels.npz` and `tiles` folder from `vietnam_building_tf` to TensorFlow Object Detection API directory `models/research/object_detection/` too. +- From `models/research/object_detection/` directory run: +```shell +python3 tf_records_generation.py --label_input=labels.npz \ + --train_rd_path=data/train_buildings.record \ + --test_rd_path=data/test_buildings.record +``` +This code will create a `train_buildings.record` and `test_buildings.record` in a folder called `data` in directory `models/research/object_detection/` + +### Object detection model setup +- download a trained model from TensorFlow. Go to [TensorFlow model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) to pick up a trained model. We used [`ssd_inception_v2_coco`](http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz). + +It's the second fastest model after `ssd_mobilenet_v1_coco`. +run this command to unzip the downloaded model: +```shell +tar -xzvf ssd_inception_v2_coco_2017_11_17.tar.gz | rm ssd_inception_v2_coco_2017_11_17.tar.gz +``` +- [Go here to copy and create a model configure file](https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/samples/configs/ssd_inception_v2_coco.config) for `ssd_inception_v2_coco` as `ssd_inception_v2_coco.config`. + +- Create label file, let's name it `building_od.pbtex`, copy and paste following content in the file: + +```JSON +item { + id: 1 + name: 'building' +} + +``` +- Go back to `ssd_inception_v2_coco.config` and chage all the `PATH_TO_BE_CONFIGURED` to the directory, e.g. at `fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"` you will need to change `PATH_TO_BE_CONFIGURED` to `ssd_inception_v2_coco_2017_11_17` in the configure file. Don't forget to change `input_path` for `train_buildings.record`, `label_map_path`, and `input_path` for `test_buildings.record` too. + +# Train the TensorFlow object detection model +You are now ready to train the model, from the `models/research/object_detection` directory, run: +```shell +python3 train.py --logtostderr \ + --train_dir=training/ \ + --pipeline_config_path=training/ssd_inception_v2_coco.config +``` +We created `training` folder to save up all the model checkpoint and output in this task, you could do the same or create a different one. +When model is successfully running you will see: +![screenshot 2018-01-24 14 30 22](https://user-images.githubusercontent.com/14057932/35352821-27df9a6a-0113-11e8-8c44-61d994d34ae6.png) + +We ran this model about **10,000 steps**, and it took **37 hours**, if you wanna run a faster model, we recommend to try out `ssd_mobilenet_v1_coco` in TensorFlow model zoo. + +# Create your object detection model to visualize +After the 10,000 steps, you will ready to create your own building detection model, by simply running: +```shell +python3 export_inference_graph.py --input_type image_tensor \ + --pipeline_config_path training/ssd_inception_v2_coco.config \ + --trained_checkpoint_prefix training/model.ckpt-9575 + --output_directory building_od_ssd_inference_graph +``` +Now we just created our building detection model inference graph, and it could visualize with `tensorboard`. +You will see the whole graph by running: +```shell +tensorboard --logdir='training' +``` +go to your web browser and paste `http://127.0.0.1:6006/`, you will see. +![screenshot 2018-01-24 14 42 00](https://user-images.githubusercontent.com/14057932/35353302-c6f555c6-0114-11e8-8c7c-1b5334e9c449.png) + +# Prediction +We created another code for you to finally run and predict any image tile you have to do the building detection. We saved the test images in a folder called `test_images`. Now [go to our Label Maker repo, copy, paste and save the code as `tf_od_predict.py`](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/tf_od_predict.py), simply by running: +```shell +python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ + --path_to_label=data/building_od.pbtxt \ + --test_image_path=test_images +``` + +This code will read through all your test image in `test_images` folder, and output final prediction in the `test_images` too. You will see the final prediction like this: +![26011-14427-15](https://user-images.githubusercontent.com/14057932/35353614-b1709390-0115-11e8-8277-08768034006d.jpg) ![25989-14427-15](https://user-images.githubusercontent.com/14057932/35353624-bacf4846-0115-11e8-9fd0-b3c75cfaaa06.jpg) From eb401a9c16bbb976dfc4775449064cd49751c546 Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Wed, 24 Jan 2018 14:59:40 -0500 Subject: [PATCH 07/20] minor text edit in readme --- examples/walkthrough-tensorflow-object-detection.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index a53a2eb..79dfe90 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -1,5 +1,7 @@ # Example Use: A building object detection with Tensor Flow +We have to walk through all these steps to be able to train a TensorFlow Object Detection and have this results: +![vietnam_od_buildings](https://user-images.githubusercontent.com/14057932/35354064-0cd453f6-0117-11e8-8e6f-96f5619bcdf3.png) # Download Training dataset @@ -130,4 +132,5 @@ python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ ``` This code will read through all your test image in `test_images` folder, and output final prediction in the `test_images` too. You will see the final prediction like this: + ![26011-14427-15](https://user-images.githubusercontent.com/14057932/35353614-b1709390-0115-11e8-8277-08768034006d.jpg) ![25989-14427-15](https://user-images.githubusercontent.com/14057932/35353624-bacf4846-0115-11e8-9fd0-b3c75cfaaa06.jpg) From 2fd728c91834833c6c2036d56f9319e60d2f40af Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Thu, 25 Jan 2018 10:03:51 -0500 Subject: [PATCH 08/20] update test and train image folders --- examples/walkthrough-tensorflow-object-detection.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index 79dfe90..a94254f 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -71,7 +71,7 @@ python3 tf_records_generation.py --label_input=labels.npz \ --train_rd_path=data/train_buildings.record \ --test_rd_path=data/test_buildings.record ``` -This code will create a `train_buildings.record` and `test_buildings.record` in a folder called `data` in directory `models/research/object_detection/` +This code will create a `train_buildings.record` and `test_buildings.record` in a folder called `data` in directory `models/research/object_detection/`. It will aslo copy images in your `tiles` folder to `images` folder and split them up into two seperate folders of `train` and `test`. ### Object detection model setup - download a trained model from TensorFlow. Go to [TensorFlow model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) to pick up a trained model. We used [`ssd_inception_v2_coco`](http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz). From 0177bb7f29e4ff5d864d8c7e2f33f318287867ab Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Thu, 25 Jan 2018 10:06:39 -0500 Subject: [PATCH 09/20] spelling check --- ...walkthrough-tensorflow-object-detection.md | 20 +++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index a94254f..51f0845 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -31,7 +31,7 @@ $ label-maker download --dest vietnam_building_tf --config vietnam_tf.json $ label-maker labels --dest vietnam_building_tf --config vietnam_tf.json ``` These commands will download and retile the OpenStreetMap QA tiles, label maker will find buildings from OpenSreetMap and draw bounding box/boxes, you could visualize the tiles in your `vietnam_building_tf/labels` folder that label maker creates. -The label tiles will be save as an numpy `labels.npz` that you will spot in the `vietnam_building_tf`folder. +The label tiles will be saved as a numpy `labels.npz` file that you will spot in the `vietnam_building_tf`folder. ![26014-14421-15](https://user-images.githubusercontent.com/14057932/35344642-a130760e-00fb-11e8-85ea-628e00c13dd5.png) ![26014-14425-15](https://user-images.githubusercontent.com/14057932/35344651-a75e74fe-00fb-11e8-95d3-3685c6fa170f.png) @@ -40,12 +40,12 @@ You could preview how the building bounding boxes are drawn on top of the RGB im ```shell $ label-maker preview -n 10 --dest vietnam_building_tf --config vietnam_tf.json ``` -You will see there are ten image tiles download in the foler `vietnam_building_tf/examples/Buildings/` +You will see there are ten image tiles download in the folder `vietnam_building_tf/examples/Buildings/` ![25989-14427-15](https://user-images.githubusercontent.com/14057932/35346218-a375d342-00ff-11e8-9ee1-8ff5c4396290.jpg) ![25991-14439-15](https://user-images.githubusercontent.com/14057932/35346236-b2c3e3ac-00ff-11e8-9ad3-250a756e852f.jpg) -The building bounding boxes overlaid with image tiles will be look like above images. -You could tell from the above image tiles that a lot of buildings in Vietnam have been mapped and without bounding boxes yet, which would impact on our model prediction accuracy. If you’d like to help improve the labelling accuracy, [start mapping on OpenStreetMap](https://www.openstreetmap.org/#map=10/20.9755/105.4118). +The building bounding boxes overlaid with image tiles will look like above images. +You could tell from the above image tiles that a lot of buildings in Vietnam have been mapped and without bounding boxes yet, which would impact on our model prediction accuracy. If you’d like to help improve the labeling accuracy, [start mapping on OpenStreetMap](https://www.openstreetmap.org/#map=10/20.9755/105.4118). Download all the image tiles that contain buildings by running ```shell $ label-maker images --dest vietnam_building_tf --config vietnam_tf.json @@ -59,8 +59,8 @@ Now, you are ready to set up Tensor Flow object detection API - `git clone https://github.com/tensorflow/models.git`; - Install TensorFlow Object Detection API by following strictly [this readme](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). You need to have everything installed, and after `python object_detection/builders/model_builder_test.py` past you will be ready to go to next step. -### Create tensorflow TFRecords training. -Tensorflow API supports a variety of file formats. TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have a python code [here on our github repo](TODO...After this branch merge) for you to create TFRecords from `labels.npz` that Label Maker created under `label-maker labels` command. +### Create TensoFlow TFRecords training. +Tensorflow API supports a variety of file formats. TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have a python code [here on our GitHub repo](TODO...After this branch merge) for you to create TFRecords from `labels.npz` that Label Maker created under `label-maker labels` command. Follow these steps to create TFRecards - Create the `tf_records_generation.py` under your Tensorflow object detection directory from [our github repo](), which is `models/research/object_detection/`. @@ -83,7 +83,7 @@ tar -xzvf ssd_inception_v2_coco_2017_11_17.tar.gz | rm ssd_inception_v2_coco_2 ``` - [Go here to copy and create a model configure file](https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/samples/configs/ssd_inception_v2_coco.config) for `ssd_inception_v2_coco` as `ssd_inception_v2_coco.config`. -- Create label file, let's name it `building_od.pbtex`, copy and paste following content in the file: +- Create label file, let's name it `building_od.pbtex`, copy and paste following content into the file: ```JSON item { @@ -102,13 +102,13 @@ python3 train.py --logtostderr \ --pipeline_config_path=training/ssd_inception_v2_coco.config ``` We created `training` folder to save up all the model checkpoint and output in this task, you could do the same or create a different one. -When model is successfully running you will see: +When the model is successfully run you will see: ![screenshot 2018-01-24 14 30 22](https://user-images.githubusercontent.com/14057932/35352821-27df9a6a-0113-11e8-8c44-61d994d34ae6.png) We ran this model about **10,000 steps**, and it took **37 hours**, if you wanna run a faster model, we recommend to try out `ssd_mobilenet_v1_coco` in TensorFlow model zoo. # Create your object detection model to visualize -After the 10,000 steps, you will ready to create your own building detection model, by simply running: +After the 10,000 steps, you will ready to create your building detection model, by simply running: ```shell python3 export_inference_graph.py --input_type image_tensor \ --pipeline_config_path training/ssd_inception_v2_coco.config \ @@ -131,6 +131,6 @@ python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ --test_image_path=test_images ``` -This code will read through all your test image in `test_images` folder, and output final prediction in the `test_images` too. You will see the final prediction like this: +This code will read through all your test image in `test_images` folder, and output the final prediction in the `test_images` too. You will see the final prediction like this: ![26011-14427-15](https://user-images.githubusercontent.com/14057932/35353614-b1709390-0115-11e8-8277-08768034006d.jpg) ![25989-14427-15](https://user-images.githubusercontent.com/14057932/35353624-bacf4846-0115-11e8-9fd0-b3c75cfaaa06.jpg) From 6230c7416c0983ec5c0aeaa84390f80cec3e1f1c Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Thu, 25 Jan 2018 11:49:14 -0500 Subject: [PATCH 10/20] install pandas --- examples/walkthrough-tensorflow-object-detection.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index 51f0845..2902505 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -6,7 +6,7 @@ We have to walk through all these steps to be able to train a TensorFlow Object # Download Training dataset Vietnam has good image through Mapbox Satellite Imagery, so we are going to use the same configure JSON file we used for [another walkthrough](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md) -- Install Label Maker by: `pip install label_maker` . +- Install Label Maker by: `pip install label_maker`; install Pandas by `pip install pandas` and install [tippecanoe](https://github.com/mapbox/tippecanoe) . - Create vietnam_tf.json for `object-detection` like shown in following json file. ```json { From e713cc81e937a00429cd0cc47077d3cf1b66ab1b Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Thu, 25 Jan 2018 14:06:14 -0500 Subject: [PATCH 11/20] add tf configure file and label --- examples/utils/building_od.pbtxt | 4 + examples/utils/ssd_inception_v2_coco.config | 191 ++++++++++++++++++ ...walkthrough-tensorflow-object-detection.md | 10 +- label_maker/label.py | 4 +- 4 files changed, 202 insertions(+), 7 deletions(-) create mode 100644 examples/utils/building_od.pbtxt create mode 100644 examples/utils/ssd_inception_v2_coco.config diff --git a/examples/utils/building_od.pbtxt b/examples/utils/building_od.pbtxt new file mode 100644 index 0000000..e38d224 --- /dev/null +++ b/examples/utils/building_od.pbtxt @@ -0,0 +1,4 @@ +item { + id: 1 + name: 'building' +} diff --git a/examples/utils/ssd_inception_v2_coco.config b/examples/utils/ssd_inception_v2_coco.config new file mode 100644 index 0000000..a2d0d5f --- /dev/null +++ b/examples/utils/ssd_inception_v2_coco.config @@ -0,0 +1,191 @@ +# SSD with Inception v2 configuration for MSCOCO Dataset. +# Users should configure the fine_tune_checkpoint field in the train config as +# well as the label_map_path and input_path fields in the train_input_reader and +# eval_input_reader. Search for "PATH_TO_BE_CONFIGURED" to find the fields that +# should be configured. + +model { + ssd { + num_classes: 90 + box_coder { + faster_rcnn_box_coder { + y_scale: 10.0 + x_scale: 10.0 + height_scale: 5.0 + width_scale: 5.0 + } + } + matcher { + argmax_matcher { + matched_threshold: 0.5 + unmatched_threshold: 0.5 + ignore_thresholds: false + negatives_lower_than_unmatched: true + force_match_for_each_row: true + } + } + similarity_calculator { + iou_similarity { + } + } + anchor_generator { + ssd_anchor_generator { + num_layers: 6 + min_scale: 0.2 + max_scale: 0.95 + aspect_ratios: 1.0 + aspect_ratios: 2.0 + aspect_ratios: 0.5 + aspect_ratios: 3.0 + aspect_ratios: 0.3333 + reduce_boxes_in_lowest_layer: true + } + } + image_resizer { + fixed_shape_resizer { + height: 300 + width: 300 + } + } + box_predictor { + convolutional_box_predictor { + min_depth: 0 + max_depth: 0 + num_layers_before_predictor: 0 + use_dropout: false + dropout_keep_probability: 0.8 + kernel_size: 3 + box_code_size: 4 + apply_sigmoid_to_scores: false + conv_hyperparams { + activation: RELU_6, + regularizer { + l2_regularizer { + weight: 0.00004 + } + } + initializer { + truncated_normal_initializer { + stddev: 0.03 + mean: 0.0 + } + } + } + } + } + feature_extractor { + type: 'ssd_inception_v2' + min_depth: 16 + depth_multiplier: 1.0 + conv_hyperparams { + activation: RELU_6, + regularizer { + l2_regularizer { + weight: 0.00004 + } + } + initializer { + truncated_normal_initializer { + stddev: 0.03 + mean: 0.0 + } + } + batch_norm { + train: true, + scale: true, + center: true, + decay: 0.9997, + epsilon: 0.001, + } + } + } + loss { + classification_loss { + weighted_sigmoid { + anchorwise_output: true + } + } + localization_loss { + weighted_smooth_l1 { + anchorwise_output: true + } + } + hard_example_miner { + num_hard_examples: 3000 + iou_threshold: 0.99 + loss_type: CLASSIFICATION + max_negatives_per_positive: 3 + min_negatives_per_image: 0 + } + classification_weight: 1.0 + localization_weight: 1.0 + } + normalize_loss_by_num_matches: true + post_processing { + batch_non_max_suppression { + score_threshold: 1e-8 + iou_threshold: 0.6 + max_detections_per_class: 100 + max_total_detections: 100 + } + score_converter: SIGMOID + } + } +} + +train_config: { + batch_size: 24 + optimizer { + rms_prop_optimizer: { + learning_rate: { + exponential_decay_learning_rate { + initial_learning_rate: 0.004 + decay_steps: 800720 + decay_factor: 0.95 + } + } + momentum_optimizer_value: 0.9 + decay: 0.9 + epsilon: 1.0 + } + } + fine_tune_checkpoint: "ssd_inception_v2_coco_2017_11_17/model.ckpt" + from_detection_checkpoint: true + # Note: The below line limits the training process to 200K steps, which we + # empirically found to be sufficient enough to train the pets dataset. This + # effectively bypasses the learning rate schedule (the learning rate will + # never decay). Remove the below line to train indefinitely. + num_steps: 200000 + data_augmentation_options { + random_horizontal_flip { + } + } + data_augmentation_options { + ssd_random_crop { + } + } +} + +train_input_reader: { + tf_record_input_reader { + input_path: "data/train_buildings.record" + } + label_map_path: "data/building_od.pbtxt" +} + +eval_config: { + num_examples: 8000 + # Note: The below line limits the evaluation process to 10 evaluations. + # Remove the below line to evaluate indefinitely. + max_evals: 10 +} + +eval_input_reader: { + tf_record_input_reader { + input_path: "data/test_buildings.record" + } + label_map_path: "data/building_od.pbtxt" + shuffle: false + num_readers: 1 + num_epochs: 1 +} diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index 79dfe90..d146f0f 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -52,7 +52,7 @@ $ label-maker images --dest vietnam_building_tf --config vietnam_tf.json ``` You will have 385 image tiles in your folder `vietnam_building_tf/tiles`You don't need to run `label-maker package` for this TensorFlow Object Detection task. -Now, you are ready to set up Tensor Flow object detection API +Now, you are ready to set up TensorFlow object detection API # Setup TensorFlow Object Detection API ### Install TensorFlow object detection: @@ -63,7 +63,7 @@ Now, you are ready to set up Tensor Flow object detection API Tensorflow API supports a variety of file formats. TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have a python code [here on our github repo](TODO...After this branch merge) for you to create TFRecords from `labels.npz` that Label Maker created under `label-maker labels` command. Follow these steps to create TFRecards -- Create the `tf_records_generation.py` under your Tensorflow object detection directory from [our github repo](), which is `models/research/object_detection/`. +- Create the `tf_records_generation.py` under your Tensorflow object detection directory,which is `models/research/object_detection/`, from [our github repo](), . - Copy and paste your `labels.npz` and `tiles` folder from `vietnam_building_tf` to TensorFlow Object Detection API directory `models/research/object_detection/` too. - From `models/research/object_detection/` directory run: ```shell @@ -92,7 +92,7 @@ item { } ``` -- Go back to `ssd_inception_v2_coco.config` and chage all the `PATH_TO_BE_CONFIGURED` to the directory, e.g. at `fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"` you will need to change `PATH_TO_BE_CONFIGURED` to `ssd_inception_v2_coco_2017_11_17` in the configure file. Don't forget to change `input_path` for `train_buildings.record`, `label_map_path`, and `input_path` for `test_buildings.record` too. +- Go back to `ssd_inception_v2_coco.config` and change all the `PATH_TO_BE_CONFIGURED` to the your file directories, e.g. at `fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"` you will need to change `PATH_TO_BE_CONFIGURED` to `ssd_inception_v2_coco_2017_11_17` in the configure file. Don't forget to change `input_path` for `train_buildings.record`, `label_map_path`, and `input_path` for `test_buildings.record` too. # Train the TensorFlow object detection model You are now ready to train the model, from the `models/research/object_detection` directory, run: @@ -108,7 +108,7 @@ When model is successfully running you will see: We ran this model about **10,000 steps**, and it took **37 hours**, if you wanna run a faster model, we recommend to try out `ssd_mobilenet_v1_coco` in TensorFlow model zoo. # Create your object detection model to visualize -After the 10,000 steps, you will ready to create your own building detection model, by simply running: +After the 10,000 steps, you will ready to create your own building detection model, simply run: ```shell python3 export_inference_graph.py --input_type image_tensor \ --pipeline_config_path training/ssd_inception_v2_coco.config \ @@ -124,7 +124,7 @@ go to your web browser and paste `http://127.0.0.1:6006/`, you will see. ![screenshot 2018-01-24 14 42 00](https://user-images.githubusercontent.com/14057932/35353302-c6f555c6-0114-11e8-8c7c-1b5334e9c449.png) # Prediction -We created another code for you to finally run and predict any image tile you have to do the building detection. We saved the test images in a folder called `test_images`. Now [go to our Label Maker repo, copy, paste and save the code as `tf_od_predict.py`](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/tf_od_predict.py), simply by running: +We created another code for you to finally run and predict any image tile you have to do the building detection. We saved the test images in a folder called `test_images`. Now [go to our Label Maker repo, copy, paste and save the code as `tf_od_predict.py`](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/tf_od_predict.py), simply run: ```shell python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ --path_to_label=data/building_od.pbtxt \ diff --git a/label_maker/label.py b/label_maker/label.py index 7271e4b..d5ea941 100644 --- a/label_maker/label.py +++ b/label_maker/label.py @@ -274,8 +274,8 @@ def _tile_results_summary(ml_type, classes): if ml_type == 'object-detection': # for each class, show number of features and number of tiles for i, cl in enumerate(classes): - cl_features = len([bb for l in labels for bb in l if bb[4] == i]) - cl_tiles = len([l for l in labels if len(list(filter(_bbox_class(i), l)))]) # pylint: disable=cell-var-from-loop + cl_features = len([bb for l in labels for bb in l if bb[4] == i + 1]) + cl_tiles = len([l for l in labels if len(list(filter(_bbox_class(i+1), l)))]) # pylint: disable=cell-var-from-loop print('{}: {} features in {} tiles'.format(cl.get('name'), cl_features, cl_tiles)) elif ml_type == 'classification': class_tile_counts = list(np.sum(labels, axis=0)) From f949d1dffbbcbcaf9dd1080241be047ea209cb36 Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Thu, 25 Jan 2018 16:39:22 -0500 Subject: [PATCH 12/20] add all images and final revision --- examples/images/preview_tiles.jpg | Bin 0 -> 88525 bytes examples/images/tensorboard_sc.jpg | Bin 0 -> 405803 bytes examples/images/tf_od_result.jpg | Bin 0 -> 87257 bytes examples/images/tf_sc.jpg | Bin 0 -> 241694 bytes examples/images/tf_tiles.jpg | Bin 0 -> 14515 bytes ...walkthrough-tensorflow-object-detection.md | 83 ++++++++++-------- 6 files changed, 48 insertions(+), 35 deletions(-) create mode 100644 examples/images/preview_tiles.jpg create mode 100644 examples/images/tensorboard_sc.jpg create mode 100644 examples/images/tf_od_result.jpg create mode 100644 examples/images/tf_sc.jpg create 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zW3Mjj=X + +

+ # Download Training dataset -Vietnam has good image through Mapbox Satellite Imagery, so we are going to use the same configure JSON file we used for [another walkthrough](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md) +Vietnam has good image through Mapbox Satellite Imagery, so we are going to use the same configure JSON file we used for [another walkthrough](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md), which we demonstrate how to train a building classifier with MXNet and Amazon SageMaker. - Install Label Maker by: `pip install label_maker`; install Pandas by `pip install pandas` and install [tippecanoe](https://github.com/mapbox/tippecanoe) . -- Create vietnam_tf.json for `object-detection` like shown in following json file. +- Create vietnam_tf.json for `object-detection` like shown in following JSON file. ```json { "country": "vietnam", @@ -21,7 +24,7 @@ Vietnam has good image through Mapbox Satellite Imagery, so we are going to use "ml_type": "object-detection" } ``` -If you don't know know what is `country`, `bounding_box`, `zoom`, `classes`, `imagery`, `background_ratio` and `ml_type`, please check other two walkthrough examples we provided: [image classification with AWS and Keras](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-aws.md) and [image classification with MXNet and SageMaker](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md). +If you don't know know what is `country`, `bounding_box`, `zoom`, `classes`, `imagery`, `background_ratio` and `ml_type`, please check [the instruction at out Label Maker GitHub repository](https://github.com/developmentseed/label-maker/blob/master/README.md) or other two walkthrough examples we provided: [image classification with AWS and Keras](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-aws.md) and [image classification with MXNet and SageMaker](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md). # Training dataset generation We'll follow the [CLI commands from the README](https://github.com/developmentseed/label-maker#command-line-use) but use a separate folder to keep our project well-managed. @@ -32,8 +35,10 @@ $ label-maker labels --dest vietnam_building_tf --config vietnam_tf.json ``` These commands will download and retile the OpenStreetMap QA tiles, label maker will find buildings from OpenSreetMap and draw bounding box/boxes, you could visualize the tiles in your `vietnam_building_tf/labels` folder that label maker creates. The label tiles will be saved as a numpy `labels.npz` file that you will spot in the `vietnam_building_tf`folder. +

+ +

-![26014-14421-15](https://user-images.githubusercontent.com/14057932/35344642-a130760e-00fb-11e8-85ea-628e00c13dd5.png) ![26014-14425-15](https://user-images.githubusercontent.com/14057932/35344651-a75e74fe-00fb-11e8-95d3-3685c6fa170f.png) You could preview how the building bounding boxes are drawn on top of the RGB image tiles by: @@ -42,36 +47,44 @@ $ label-maker preview -n 10 --dest vietnam_building_tf --config vietnam_tf.json ``` You will see there are ten image tiles download in the folder `vietnam_building_tf/examples/Buildings/` -![25989-14427-15](https://user-images.githubusercontent.com/14057932/35346218-a375d342-00ff-11e8-9ee1-8ff5c4396290.jpg) ![25991-14439-15](https://user-images.githubusercontent.com/14057932/35346236-b2c3e3ac-00ff-11e8-9ad3-250a756e852f.jpg) +

+ +

+ + The building bounding boxes overlaid with image tiles will look like above images. You could tell from the above image tiles that a lot of buildings in Vietnam have been mapped and without bounding boxes yet, which would impact on our model prediction accuracy. If you’d like to help improve the labeling accuracy, [start mapping on OpenStreetMap](https://www.openstreetmap.org/#map=10/20.9755/105.4118). -Download all the image tiles that contain buildings by running + +To download all the image tiles that contain buildings by running ```shell $ label-maker images --dest vietnam_building_tf --config vietnam_tf.json ``` -You will have 385 image tiles in your folder `vietnam_building_tf/tiles`You don't need to run `label-maker package` for this TensorFlow Object Detection task. +You will have 385 image tiles in your folder `vietnam_building_tf/tiles`You don't need to run `label-maker package` for this TensorFlow Object Detection task. We have another code for you, to finish this task, and just follow the following instruction to finish the task. Now, you are ready to set up TensorFlow object detection API # Setup TensorFlow Object Detection API + ### Install TensorFlow object detection: -- `git clone https://github.com/tensorflow/models.git`; +- In your terminal, type in `git clone https://github.com/tensorflow/models.git`; - Install TensorFlow Object Detection API by following strictly [this readme](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). You need to have everything installed, and after `python object_detection/builders/model_builder_test.py` past you will be ready to go to next step. -### Create TensoFlow TFRecords training. -Tensorflow API supports a variety of file formats. TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have a python code [here on our GitHub repo](TODO...After this branch merge) for you to create TFRecords from `labels.npz` that Label Maker created under `label-maker labels` command. +### Create TensoFlow TFRecords for model training +Tensorflow API supports a variety of file formats. TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have a python code [here on our GitHub repo](TODO...After this branch merge) for you to create TFRecords from `labels.npz` that Label Maker created after `label-maker labels` command. + +Follow these steps to create TFRecards. +**From now on we will refer the TensorFlow Object Detection Directionary `models/research/object_detection/` to TOD directory.** +- Create the `tf_records_generation.py` under TOD directory, from [our github repo](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/tf_records_generation.py), . +- Copy and paste your `labels.npz` and `tiles` folders from `vietnam_building_tf` to TOD directory too. +- From TOD directory run: -Follow these steps to create TFRecards -- Create the `tf_records_generation.py` under your Tensorflow object detection directory,which is `models/research/object_detection/`, from [our github repo](), . -- Copy and paste your `labels.npz` and `tiles` folder from `vietnam_building_tf` to TensorFlow Object Detection API directory `models/research/object_detection/` too. -- From `models/research/object_detection/` directory run: ```shell python3 tf_records_generation.py --label_input=labels.npz \ --train_rd_path=data/train_buildings.record \ --test_rd_path=data/test_buildings.record ``` -This code will create a `train_buildings.record` and `test_buildings.record` in a folder called `data` in directory `models/research/object_detection/`. It will aslo copy images in your `tiles` folder to `images` folder and split them up into two seperate folders of `train` and `test`. +This code will create a `train_buildings.record` and `test_buildings.record` in a folder called `data` in TOD directory. It will also copy images in your `tiles` folder to `images` folder and split them up into two separate folders of `train` and `test`. ### Object detection model setup - download a trained model from TensorFlow. Go to [TensorFlow model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) to pick up a trained model. We used [`ssd_inception_v2_coco`](http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz). @@ -81,38 +94,31 @@ run this command to unzip the downloaded model: ```shell tar -xzvf ssd_inception_v2_coco_2017_11_17.tar.gz | rm ssd_inception_v2_coco_2017_11_17.tar.gz ``` -- [Go here to copy and create a model configure file](https://raw.githubusercontent.com/tensorflow/models/master/research/object_detection/samples/configs/ssd_inception_v2_coco.config) for `ssd_inception_v2_coco` as `ssd_inception_v2_coco.config`. - -- Create label file, let's name it `building_od.pbtex`, copy and paste following content into the file: +- [Go to our GitHub repository and create a model configure file](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/ssd_inception_v2_coco.config) for `ssd_inception_v2_coco` as `ssd_inception_v2_coco.config`, create a folder, let's called it `training` and save this model configure in the folder. -```JSON -item { - id: 1 - name: 'building' -} +- [Go to our GitHub repository and create a model configure file](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/building_od.pbtxt) to get `building_od.pbtex` too. -``` -- Go back to `ssd_inception_v2_coco.config` and change all the `PATH_TO_BE_CONFIGURED` to the your file directories, e.g. at `fine_tune_checkpoint: "PATH_TO_BE_CONFIGURED/model.ckpt"` you will need to change `PATH_TO_BE_CONFIGURED` to `ssd_inception_v2_coco_2017_11_17` in the configure file. Don't forget to change `input_path` for `train_buildings.record`, `label_map_path`, and `input_path` for `test_buildings.record` too. +- if you set up the model the exact way we did here, you are good to go to train the model. # Train the TensorFlow object detection model You are now ready to train the model, from the `models/research/object_detection` directory, run: + ```shell python3 train.py --logtostderr \ --train_dir=training/ \ --pipeline_config_path=training/ssd_inception_v2_coco.config ``` -We created `training` folder to save up all the model checkpoint and output in this task, you could do the same or create a different one. +We saved up all the model checkpoint and output in this task to `training` folder. When the model is successfully run you will see: -![screenshot 2018-01-24 14 30 22](https://user-images.githubusercontent.com/14057932/35352821-27df9a6a-0113-11e8-8c44-61d994d34ae6.png) +

+ +

We ran this model about **10,000 steps**, and it took **37 hours**, if you wanna run a faster model, we recommend to try out `ssd_mobilenet_v1_coco` in TensorFlow model zoo. # Create your object detection model to visualize -<<<<<<< HEAD After the 10,000 steps, you will ready to create your own building detection model, simply run: -======= -After the 10,000 steps, you will ready to create your building detection model, by simply running: ->>>>>>> 6230c7416c0983ec5c0aeaa84390f80cec3e1f1c + ```shell python3 export_inference_graph.py --input_type image_tensor \ --pipeline_config_path training/ssd_inception_v2_coco.config \ @@ -121,14 +127,19 @@ python3 export_inference_graph.py --input_type image_tensor \ ``` Now we just created our building detection model inference graph, and it could visualize with `tensorboard`. You will see the whole graph by running: + ```shell tensorboard --logdir='training' ``` go to your web browser and paste `http://127.0.0.1:6006/`, you will see. -![screenshot 2018-01-24 14 42 00](https://user-images.githubusercontent.com/14057932/35353302-c6f555c6-0114-11e8-8c7c-1b5334e9c449.png) +

+ +

+ # Prediction We created another code for you to finally run and predict any image tile you have to do the building detection. We saved the test images in a folder called `test_images`. Now [go to our Label Maker repo, copy, paste and save the code as `tf_od_predict.py`](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/tf_od_predict.py), simply run: + ```shell python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ --path_to_label=data/building_od.pbtxt \ @@ -137,4 +148,6 @@ python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ This code will read through all your test image in `test_images` folder, and output the final prediction in the `test_images` too. You will see the final prediction like this: -![26011-14427-15](https://user-images.githubusercontent.com/14057932/35353614-b1709390-0115-11e8-8277-08768034006d.jpg) ![25989-14427-15](https://user-images.githubusercontent.com/14057932/35353624-bacf4846-0115-11e8-9fd0-b3c75cfaaa06.jpg) +

+ +

From bbcfd8fa6c210a455ecde2007ad8130e4778a628 Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Thu, 25 Jan 2018 16:41:20 -0500 Subject: [PATCH 13/20] name fixing --- examples/walkthrough-tensorflow-object-detection.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index 44c7c99..85931af 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -1,4 +1,4 @@ -# Example Use: A building object detection with Tensor Flow +# Example Use: A building object detection with TensorFlow API You have to walk through all these steps to be able to train a TensorFlow Object Detection and have the following results:

From 4163c4eb8cffd43b05931100fa2dd807a3b19fbb Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Fri, 26 Jan 2018 10:49:08 -0500 Subject: [PATCH 14/20] from numpy to tfrecords no pandas --- examples/utils/tf_records_generation.py | 36 +++++++++---------------- 1 file changed, 13 insertions(+), 23 deletions(-) diff --git a/examples/utils/tf_records_generation.py b/examples/utils/tf_records_generation.py index 9867e62..bd5f0fd 100644 --- a/examples/utils/tf_records_generation.py +++ b/examples/utils/tf_records_generation.py @@ -19,9 +19,7 @@ from os import makedirs, path as op import shutil -import pandas as pd import tensorflow as tf - from PIL import Image from utils import dataset_util from collections import namedtuple @@ -36,6 +34,7 @@ # TO-DO replace this with label map def class_text_to_int(row_label): + """read in label data""" if row_label == 'building': return 1 return None @@ -95,6 +94,7 @@ def create_tf_example(group, path): def main(_): + """coverts numpy array into tfrecord files, the numpy array is in order of 'filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'""" labels = np.load(op.join(os.getcwd(), FLAGS.label_input)) tile_names = [tile for tile in labels.files] tile_names.sort() @@ -121,18 +121,12 @@ def main(_): y = ["{}.jpg".format(tile), width, height, cl_str, bbox[0], bbox[1], bbox[2], bbox[3]] tf_tiles_info.append(y) #train_len = 0.8 + tf_array = np.array(tf_tiles_info) split_index = int(len(tf_tiles_info) *0.8 ) - column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] - df = pd.DataFrame(tf_tiles_info, columns=column_name) - #shuffle the dataframe - df = df.sample(frac=1) - train_df = df[:split_index] - test_df = df[split_index:] + tf_train = tf_array[0: split_index] + tf_test = tf_array[split_index, :] print("You have {} training tiles and {} test tiles ready".format( - len(set(train_df['filename'])), len(set(test_df['filename'])))) - # train_df.to_csv("train_df.csv") - # test_df.to_csv("test_df.csv") - + len(set(list(tf_train[:,1]))), len(set(list(tf_test[:,1]))))) tiles_dir = op.join(os.getcwd(), 'tiles') train_dir = op.join(os.getcwd(), 'images', 'train') test_dir = op.join(os.getcwd(), 'images', 'test') @@ -142,31 +136,27 @@ def main(_): if not op.isdir(test_dir): makedirs(test_dir) - for tile in train_df['filename']: + for tile in list(tf_train[:,1]): tile_dir = op.join(tiles_dir, tile) shutil.copy(tile_dir, train_dir) - for tile in test_df['filename']: + # for tile in test_df['filename']: + for tile in list(tf_test[:,1]): tile_dir = op.join(tiles_dir, tile) shutil.copy(tile_dir, test_dir) ### for train writer = tf.python_io.TFRecordWriter(FLAGS.train_rd_path) - grouped = split(train_df, 'filename') - - for group in grouped: - tf_example = create_tf_example(group, train_dir) - writer.write(tf_example.SerializeToString()) + tf_example = create_tf_example(tf_train, train_dir) + writer.write(tf_example.SerializeToString()) writer.close() output_train= op.join(os.getcwd(),FLAGS.train_rd_path) print('Successfully created the TFRecords: {}'.format(output_train)) ### for test writer = tf.python_io.TFRecordWriter(FLAGS.test_rd_path) - grouped = split(test_df, 'filename') - for group in grouped: - tf_example = create_tf_example(group, test_dir) - writer.write(tf_example.SerializeToString()) + tf_example = create_tf_example(tf_test, test_dir) + writer.write(tf_example.SerializeToString()) writer.close() output_test = op.join(os.getcwd(),FLAGS.test_rd_path) print('Successfully created the TFRecords: {}'.format(output_test)) From e0ae3c36779ed9fed68a57a265fe2b124b87fdb3 Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Fri, 26 Jan 2018 10:59:40 -0500 Subject: [PATCH 15/20] kick out pandas installation --- examples/walkthrough-tensorflow-object-detection.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index 85931af..c1e2391 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -9,7 +9,7 @@ You have to walk through all these steps to be able to train a TensorFlow Object # Download Training dataset Vietnam has good image through Mapbox Satellite Imagery, so we are going to use the same configure JSON file we used for [another walkthrough](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md), which we demonstrate how to train a building classifier with MXNet and Amazon SageMaker. -- Install Label Maker by: `pip install label_maker`; install Pandas by `pip install pandas` and install [tippecanoe](https://github.com/mapbox/tippecanoe) . +- Install Label Maker by: `pip install label_maker`; and install [tippecanoe](https://github.com/mapbox/tippecanoe) . - Create vietnam_tf.json for `object-detection` like shown in following JSON file. ```json { From 2edd1fe70a43b5b646662ef6603f579275141fae Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Tue, 30 Jan 2018 11:03:45 -0500 Subject: [PATCH 16/20] tf records generation --- examples/utils/tf_records_generation.py | 40 ++++++++++++++++--------- 1 file changed, 26 insertions(+), 14 deletions(-) diff --git a/examples/utils/tf_records_generation.py b/examples/utils/tf_records_generation.py index bd5f0fd..2aaf6c3 100644 --- a/examples/utils/tf_records_generation.py +++ b/examples/utils/tf_records_generation.py @@ -4,10 +4,12 @@ To use this code with Label Maker and Tensor Flow object detection API, this code work similar to `label-maker package`. To create a correct training data set for Tensor Flow Object Detecrtion, we recommend you do: + 1. After running `label-maker images`, do `git clone https://github.com/tensorflow/models.git` 2. Install TensorFlow object detection by following this: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md -3. From your Label Maker, copy `tiles` folder, this code `tf_records_generation.py` and `labels.npz` to Tensorflow object detecrtion directory `tensorflow/models/research/` +3. From your Label Maker, copy `tiles` folder, this code `tf_records_generation.py` and `labels.py` to Tensorflow object detecrtion directory 4. From directory `tensorflow/models/research/` simply runs: + python3 tf_records_generation.py --label_input=labels.npz \ --train_rd_path=data/train_buildings.record \ --test_rd_path=data/test_buildings.record @@ -19,7 +21,9 @@ from os import makedirs, path as op import shutil +import pandas as pd import tensorflow as tf + from PIL import Image from utils import dataset_util from collections import namedtuple @@ -34,7 +38,6 @@ # TO-DO replace this with label map def class_text_to_int(row_label): - """read in label data""" if row_label == 'building': return 1 return None @@ -94,7 +97,6 @@ def create_tf_example(group, path): def main(_): - """coverts numpy array into tfrecord files, the numpy array is in order of 'filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'""" labels = np.load(op.join(os.getcwd(), FLAGS.label_input)) tile_names = [tile for tile in labels.files] tile_names.sort() @@ -121,12 +123,18 @@ def main(_): y = ["{}.jpg".format(tile), width, height, cl_str, bbox[0], bbox[1], bbox[2], bbox[3]] tf_tiles_info.append(y) #train_len = 0.8 - tf_array = np.array(tf_tiles_info) split_index = int(len(tf_tiles_info) *0.8 ) - tf_train = tf_array[0: split_index] - tf_test = tf_array[split_index, :] + column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] + df = pd.DataFrame(tf_tiles_info, columns=column_name) + #shuffle the dataframe + df = df.sample(frac=1) + train_df = df[:split_index] + test_df = df[split_index:] print("You have {} training tiles and {} test tiles ready".format( - len(set(list(tf_train[:,1]))), len(set(list(tf_test[:,1]))))) + len(set(train_df['filename'])), len(set(test_df['filename'])))) + # train_df.to_csv("train_df.csv") + # test_df.to_csv("test_df.csv") + tiles_dir = op.join(os.getcwd(), 'tiles') train_dir = op.join(os.getcwd(), 'images', 'train') test_dir = op.join(os.getcwd(), 'images', 'test') @@ -136,27 +144,31 @@ def main(_): if not op.isdir(test_dir): makedirs(test_dir) - for tile in list(tf_train[:,1]): + for tile in train_df['filename']: tile_dir = op.join(tiles_dir, tile) shutil.copy(tile_dir, train_dir) - # for tile in test_df['filename']: - for tile in list(tf_test[:,1]): + for tile in test_df['filename']: tile_dir = op.join(tiles_dir, tile) shutil.copy(tile_dir, test_dir) ### for train writer = tf.python_io.TFRecordWriter(FLAGS.train_rd_path) - tf_example = create_tf_example(tf_train, train_dir) - writer.write(tf_example.SerializeToString()) + grouped = split(train_df, 'filename') + + for group in grouped: + tf_example = create_tf_example(group, train_dir) + writer.write(tf_example.SerializeToString()) writer.close() output_train= op.join(os.getcwd(),FLAGS.train_rd_path) print('Successfully created the TFRecords: {}'.format(output_train)) ### for test writer = tf.python_io.TFRecordWriter(FLAGS.test_rd_path) + grouped = split(test_df, 'filename') + for group in grouped: + tf_example = create_tf_example(group, test_dir) + writer.write(tf_example.SerializeToString()) - tf_example = create_tf_example(tf_test, test_dir) - writer.write(tf_example.SerializeToString()) writer.close() output_test = op.join(os.getcwd(),FLAGS.test_rd_path) print('Successfully created the TFRecords: {}'.format(output_test)) From 24070e4882f266e299c3550aa15dcb0ee0b5839b Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Tue, 30 Jan 2018 15:13:34 -0500 Subject: [PATCH 17/20] add clamp function --- examples/utils/tf_records_generation.py | 9 +-------- examples/walkthrough-tensorflow-object-detection.md | 3 ++- 2 files changed, 3 insertions(+), 9 deletions(-) diff --git a/examples/utils/tf_records_generation.py b/examples/utils/tf_records_generation.py index 2aaf6c3..42d3b83 100644 --- a/examples/utils/tf_records_generation.py +++ b/examples/utils/tf_records_generation.py @@ -112,14 +112,7 @@ def main(_): for bbox in bboxes: if bbox[4] == 1: cl_str = "building" - if bbox[0] < 0: - bbox[0] = 0 - if bbox[1] < 0: - bbox[1] = 0 - if bbox[2] > 256: - bbox[2] = 256 - if bbox[3] > 256: - bbox[3] = 256 + bbox = [max(0, min(255, x)) for x in bbox[0:4]] y = ["{}.jpg".format(tile), width, height, cl_str, bbox[0], bbox[1], bbox[2], bbox[3]] tf_tiles_info.append(y) #train_len = 0.8 diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index c1e2391..e82fb00 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -1,5 +1,6 @@ # Example Use: A building object detection with TensorFlow API + Google TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. In this example, we use it to detect buildings in Vietnam. You have to walk through all these steps to be able to train a TensorFlow Object Detection and have the following results:

@@ -92,7 +93,7 @@ This code will create a `train_buildings.record` and `test_buildings.record` in It's the second fastest model after `ssd_mobilenet_v1_coco`. run this command to unzip the downloaded model: ```shell -tar -xzvf ssd_inception_v2_coco_2017_11_17.tar.gz | rm ssd_inception_v2_coco_2017_11_17.tar.gz +tar -xzvf ssd_inception_v2_coco_2017_11_17.tar.gz && rm ssd_inception_v2_coco_2017_11_17.tar.gz ``` - [Go to our GitHub repository and create a model configure file](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/ssd_inception_v2_coco.config) for `ssd_inception_v2_coco` as `ssd_inception_v2_coco.config`, create a folder, let's called it `training` and save this model configure in the folder. From 37375b963e6a042048a74148729ed0c8fe3fc933 Mon Sep 17 00:00:00 2001 From: Drew Bollinger Date: Tue, 30 Jan 2018 16:57:59 -0500 Subject: [PATCH 18/20] Copy updates --- ...walkthrough-tensorflow-object-detection.md | 137 +++++++++--------- 1 file changed, 71 insertions(+), 66 deletions(-) diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index e82fb00..ba89a5b 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -1,17 +1,18 @@ -# Example Use: A building object detection with TensorFlow API +# Example Use: A building detector with TensorFlow API + +Google TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. In this example, we use it to detect buildings in Vietnam. If you follow these steps, you'll be able to train a TensorFlow Object Detection model with the following results: - Google TensorFlow Object Detection API is an open source framework built on top of TensorFlow that makes it easy to construct, train, and deploy object detection models. In this example, we use it to detect buildings in Vietnam. -You have to walk through all these steps to be able to train a TensorFlow Object Detection and have the following results:

+First install Label Maker (`pip install label-maker`) and [tippecanoe](https://github.com/mapbox/tippecanoe) + +## Create the training dataset +Vietnam has good imagery via the Mapbox Satellite layer, so we are going to use the same configuration file we used for [another walkthrough](walkthrough-classification-mxnet-sagemaker.md), which we used to train a building classifier with MXNet and Amazon SageMaker. -# Download Training dataset -Vietnam has good image through Mapbox Satellite Imagery, so we are going to use the same configure JSON file we used for [another walkthrough](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md), which we demonstrate how to train a building classifier with MXNet and Amazon SageMaker. -- Install Label Maker by: `pip install label_maker`; and install [tippecanoe](https://github.com/mapbox/tippecanoe) . -- Create vietnam_tf.json for `object-detection` like shown in following JSON file. +Create `config.json` as shown in following JSON file. ```json { "country": "vietnam", @@ -25,129 +26,133 @@ Vietnam has good image through Mapbox Satellite Imagery, so we are going to use "ml_type": "object-detection" } ``` -If you don't know know what is `country`, `bounding_box`, `zoom`, `classes`, `imagery`, `background_ratio` and `ml_type`, please check [the instruction at out Label Maker GitHub repository](https://github.com/developmentseed/label-maker/blob/master/README.md) or other two walkthrough examples we provided: [image classification with AWS and Keras](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-aws.md) and [image classification with MXNet and SageMaker](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/walkthrough-classification-mxnet-sagemaker.md). - -# Training dataset generation -We'll follow the [CLI commands from the README](https://github.com/developmentseed/label-maker#command-line-use) but use a separate folder to keep our project well-managed. +If you're curious about these different options, check out [the README](../README.md#configuration) or the other examples. Now that we've configured our project, we'll start with the [CLI commands](../README.md#command-line-use). ```shell -$ label-maker download --dest vietnam_building_tf --config vietnam_tf.json -$ label-maker labels --dest vietnam_building_tf --config vietnam_tf.json +$ label-maker download +$ label-maker labels ``` -These commands will download and retile the OpenStreetMap QA tiles, label maker will find buildings from OpenSreetMap and draw bounding box/boxes, you could visualize the tiles in your `vietnam_building_tf/labels` folder that label maker creates. -The label tiles will be saved as a numpy `labels.npz` file that you will spot in the `vietnam_building_tf`folder. +These commands will first download and retile the OpenStreetMap QA tiles. Then it will create a label file in `data/labels.npz` with the bounding box for each building. Finally, you can visualize the bounding boxes in the `data/labels` folder that label maker creates. +

- -You could preview how the building bounding boxes are drawn on top of the RGB image tiles by: +You can preview how the building bounding boxes are drawn on top of the RGB image tiles with this command: ```shell -$ label-maker preview -n 10 --dest vietnam_building_tf --config vietnam_tf.json +$ label-maker preview -n 10 ``` -You will see there are ten image tiles download in the folder `vietnam_building_tf/examples/Buildings/` +This will download ten images to the folder `data/examples/Buildings/`

+You can tell from the above image tiles that some buildings in Vietnam haven't been mapped yet which will impact our model prediction accuracy. If you’d like to help improve the labeling accuracy, [start mapping on OpenStreetMap](https://www.openstreetmap.org/#map=10/20.9755/105.4118). - -The building bounding boxes overlaid with image tiles will look like above images. -You could tell from the above image tiles that a lot of buildings in Vietnam have been mapped and without bounding boxes yet, which would impact on our model prediction accuracy. If you’d like to help improve the labeling accuracy, [start mapping on OpenStreetMap](https://www.openstreetmap.org/#map=10/20.9755/105.4118). - -To download all the image tiles that contain buildings by running +To download all the image tiles that contain buildings: ```shell -$ label-maker images --dest vietnam_building_tf --config vietnam_tf.json +$ label-maker images ``` -You will have 385 image tiles in your folder `vietnam_building_tf/tiles`You don't need to run `label-maker package` for this TensorFlow Object Detection task. We have another code for you, to finish this task, and just follow the following instruction to finish the task. +You will have 385 image tiles in your folder `data/tiles`. You don't need to run `label-maker package` for the TensorFlow Object Detection task. We'll use some custom code (included) to write the images and labels to a different format. -Now, you are ready to set up TensorFlow object detection API +Now, you are ready to set up the TensorFlow Object Detection API. -# Setup TensorFlow Object Detection API +## Setup TensorFlow Object Detection API ### Install TensorFlow object detection: -- In your terminal, type in `git clone https://github.com/tensorflow/models.git`; -- Install TensorFlow Object Detection API by following strictly [this readme](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). You need to have everything installed, and after `python object_detection/builders/model_builder_test.py` past you will be ready to go to next step. +- Download the necessary scripts with `git clone https://github.com/tensorflow/models.git` +- Install TensorFlow Object Detection API by strictly following [these instructions](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md). Once you've successfully run `python object_detection/builders/model_builder_test.py` you are ready for the next step. +- To access the necessary utility scripts, you'll need to run all the following commands from the `models/research/object_detection` directory from the cloned repo. **From here on we will refer the TensorFlow Object Detection directory `models/research/object_detection/` as the TOD directory.** + +### Create TFRecords for model training +Tensorflow API supports a variety of file formats. The TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have a python code [in this repo](../utils/tf_records_generation.py) which converts the `labels.npz` file to a TFRecords file. -### Create TensoFlow TFRecords for model training -Tensorflow API supports a variety of file formats. TFRecord file format is a simple record-oriented binary format that many TensorFlow applications use. We have a python code [here on our GitHub repo](TODO...After this branch merge) for you to create TFRecords from `labels.npz` that Label Maker created after `label-maker labels` command. +Follow these steps to create TFRecords. -Follow these steps to create TFRecards. -**From now on we will refer the TensorFlow Object Detection Directionary `models/research/object_detection/` to TOD directory.** -- Create the `tf_records_generation.py` under TOD directory, from [our github repo](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/tf_records_generation.py), . -- Copy and paste your `labels.npz` and `tiles` folders from `vietnam_building_tf` to TOD directory too. -- From TOD directory run: +- Copy `tf_records_generation.py` from [this repo](../utils/tf_records_generation.py) to the TOD directory, . +- Copy your `labels.npz` file and `tiles` folders from `data` to the TOD directory. +- From the TOD directory run: ```shell python3 tf_records_generation.py --label_input=labels.npz \ --train_rd_path=data/train_buildings.record \ --test_rd_path=data/test_buildings.record ``` -This code will create a `train_buildings.record` and `test_buildings.record` in a folder called `data` in TOD directory. It will also copy images in your `tiles` folder to `images` folder and split them up into two separate folders of `train` and `test`. +This will create `train_buildings.record` and `test_buildings.record` files in a folder called `data` in the TOD directory. It will also copy images in your `tiles` folder to an `images` folder and split them up into two separate folders of `train` and `test`. ### Object detection model setup -- download a trained model from TensorFlow. Go to [TensorFlow model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) to pick up a trained model. We used [`ssd_inception_v2_coco`](http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz). +Now we're ready to set up the model architecture. For this walkthrough, we'll download a pre-trained model from the [TensorFlow model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md). We'll demonstrate using [`ssd_inception_v2_coco`](http://download.tensorflow.org/models/object_detection/ssd_inception_v2_coco_2017_11_17.tar.gz) (download link): + - Download the model, unzip, and move the folder to the TOD directory + - Create a new folder `training` in the TOD directory. + - Copy a [model configuration file](utils/ssd_inception_v2_coco.config) to the `training directory`. If you aren't using `ssd_inception_v2_coco`, you'll need to update the configuration file to match your selected model. + - Copy a [class definitions file](/utils/building_od.pbtxt) to the `data` directory. -It's the second fastest model after `ssd_mobilenet_v1_coco`. -run this command to unzip the downloaded model: -```shell -tar -xzvf ssd_inception_v2_coco_2017_11_17.tar.gz && rm ssd_inception_v2_coco_2017_11_17.tar.gz -``` -- [Go to our GitHub repository and create a model configure file](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/ssd_inception_v2_coco.config) for `ssd_inception_v2_coco` as `ssd_inception_v2_coco.config`, create a folder, let's called it `training` and save this model configure in the folder. +Now your current directory should be `models/research/object_detection/` and in addition to the files included in that repo originally, your folder structure should look like this: -- [Go to our GitHub repository and create a model configure file](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/building_od.pbtxt) to get `building_od.pbtex` too. +``` +models/research/object_detection/ +├── ssd_inception_v2_coco/ +├── training/ +│ └── ssd_inception_v2_coco.config +├── data/ +│ ├── train_buildings.record +│ ├── test_buildings.record +│ └── building_od.pbtxt +└── images/ + ├── train/ + └── test/ -- if you set up the model the exact way we did here, you are good to go to train the model. +``` # Train the TensorFlow object detection model -You are now ready to train the model, from the `models/research/object_detection` directory, run: +You are now ready to train the model. From the `models/research/object_detection` directory, run: ```shell -python3 train.py --logtostderr \ +python train.py --logtostderr \ --train_dir=training/ \ --pipeline_config_path=training/ssd_inception_v2_coco.config ``` -We saved up all the model checkpoint and output in this task to `training` folder. -When the model is successfully run you will see: +The model checkpoints and outputs for this task will save in the `training` folder. When the model is running successfully you will see: +

-We ran this model about **10,000 steps**, and it took **37 hours**, if you wanna run a faster model, we recommend to try out `ssd_mobilenet_v1_coco` in TensorFlow model zoo. +We ran this model for about **10,000 steps**, and it took **37 hours** (local CPU). If you want to run a faster model, we recommend trying `ssd_mobilenet_v1_coco` in TensorFlow model zoo, training on a GPU, or using Google's [Cloud Machine Learning Engine](https://cloud.google.com/ml-engine/). -# Create your object detection model to visualize -After the 10,000 steps, you will ready to create your own building detection model, simply run: +## Visualize the Model +Create the building detection model inference graph with: ```shell -python3 export_inference_graph.py --input_type image_tensor \ +python export_inference_graph.py --input_type image_tensor \ --pipeline_config_path training/ssd_inception_v2_coco.config \ --trained_checkpoint_prefix training/model.ckpt-9575 - --output_directory building_od_ssd_inference_graph + --output_directory building_od_ssd ``` -Now we just created our building detection model inference graph, and it could visualize with `tensorboard`. -You will see the whole graph by running: +We can visualize this graph using [`tensorboard`](https://github.com/tensorflow/tensorboard): ```shell tensorboard --logdir='training' ``` -go to your web browser and paste `http://127.0.0.1:6006/`, you will see. + +Go to `http://127.0.0.1:6006/` in your web browser and you will see: +

- -# Prediction -We created another code for you to finally run and predict any image tile you have to do the building detection. We saved the test images in a folder called `test_images`. Now [go to our Label Maker repo, copy, paste and save the code as `tf_od_predict.py`](https://github.com/developmentseed/label-maker/blob/tf_object_detection/examples/utils/tf_od_predict.py), simply run: +## Prediction +Now let's run the model over our test tiles to predict where buildings are. Copy [this script](utils/tf_od_predict.py) to the TOD directory then run: ```shell -python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ - --path_to_label=data/building_od.pbtxt \ - --test_image_path=test_images +python tf_od_predict.py --model_name=building_od_ssd \ + --path_to_label=data/building_od.pbtxt \ + --test_image_path=images/test ``` -This code will read through all your test image in `test_images` folder, and output the final prediction in the `test_images` too. You will see the final prediction like this: +This code will read through all your test images in `images/test` folder and output the final prediction into the same folder. You will see a final prediction like this:

From b9c1cb2b6f630dda7e6de5f7f69b18091143198e Mon Sep 17 00:00:00 2001 From: Drew Bollinger Date: Tue, 30 Jan 2018 17:03:59 -0500 Subject: [PATCH 19/20] Small code edits --- examples/utils/tf_od_predict.py | 14 ++++----- examples/utils/tf_records_generation.py | 41 ++++++++++--------------- 2 files changed, 23 insertions(+), 32 deletions(-) diff --git a/examples/utils/tf_od_predict.py b/examples/utils/tf_od_predict.py index 3b602ec..2209245 100644 --- a/examples/utils/tf_od_predict.py +++ b/examples/utils/tf_od_predict.py @@ -1,11 +1,11 @@ """ -This is adepted from Tensorflow (https://github.com/tensorflow/models/tree/master/research/object_detection); -Save this code under the diractory of `models/research/object_detection/` +This is adapted from Tensorflow (https://github.com/tensorflow/models/tree/master/research/object_detection); +Save this code under the directory `models/research/object_detection/` To use, run: -python3 tf_od_predict.py --model_name=building_od_ssd_inference_graph \ - --path_to_label=data/building_od.pbtxt \ - --test_image_path=test_images +python tf_od_predict.py --model_name=building_od_ssd \ + --path_to_label=data/building_od.pbtxt \ + --test_image_path=test_images """ import os @@ -28,8 +28,6 @@ from utils import label_map_util from utils import visualization_utils as vis_util - - flags = tf.app.flags flags.DEFINE_string('model_name', '', 'Path to frozen detection graph') flags.DEFINE_string('path_to_label', '', 'Path to label file') @@ -95,7 +93,7 @@ def tf_od_pred(): test_imgs = glob.glob(test_image_path + "/*.jpg") ############ - #Load the frozen tensorflow model + #Load the frozen tensorflow model ############# detection_graph = tf.Graph() diff --git a/examples/utils/tf_records_generation.py b/examples/utils/tf_records_generation.py index 42d3b83..c9081aa 100644 --- a/examples/utils/tf_records_generation.py +++ b/examples/utils/tf_records_generation.py @@ -2,15 +2,15 @@ This code was modified on top of Google tensorflow (https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/using_your_own_dataset.md) -To use this code with Label Maker and Tensor Flow object detection API, this code work similar to `label-maker package`. -To create a correct training data set for Tensor Flow Object Detecrtion, we recommend you do: +This code works similar to `label-maker package` when used with Label Maker and Tensor Flow object detection API. +To create a correct training data set for Tensor Flow Object Detection, we recommend you: 1. After running `label-maker images`, do `git clone https://github.com/tensorflow/models.git` 2. Install TensorFlow object detection by following this: https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/installation.md 3. From your Label Maker, copy `tiles` folder, this code `tf_records_generation.py` and `labels.py` to Tensorflow object detecrtion directory -4. From directory `tensorflow/models/research/` simply runs: +4. From directory `tensorflow/models/research/` run: -python3 tf_records_generation.py --label_input=labels.npz \ +python tf_records_generation.py --label_input=labels.npz \ --train_rd_path=data/train_buildings.record \ --test_rd_path=data/test_buildings.record """ @@ -28,21 +28,17 @@ from utils import dataset_util from collections import namedtuple - flags = tf.app.flags flags.DEFINE_string('label_input', '', 'Path to the labels.npz input') flags.DEFINE_string('train_rd_path', '', 'Path to output TFRecord') flags.DEFINE_string('test_rd_path', '', 'Path to output TFRecord') FLAGS = flags.FLAGS - -# TO-DO replace this with label map def class_text_to_int(row_label): if row_label == 'building': return 1 return None - def split(df, group): data = namedtuple('data', ['filename', 'object']) gb = df.groupby(group) @@ -51,12 +47,12 @@ def split(df, group): def create_tf_example(group, path): """Creates a tf.Example proto from sample buillding image tile. -Args: - encoded_building_image_data: The jpg encoded data of the building image. + Args: + encoded_building_image_data: The jpg encoded data of the building image. -Returns: - example: The created tf.Example. -""" + Returns: + example: The created tf.Example. + """ with tf.gfile.GFile(op.join(path, '{}'.format(group.filename)), 'rb') as fid: encoded_jpg = fid.read() encoded_jpg_io = io.BytesIO(encoded_jpg) @@ -108,25 +104,23 @@ def main(_): bboxes = labels[tile].tolist() width = 256 height = 256 - if len(bboxes) != 0: + if bboxes: for bbox in bboxes: if bbox[4] == 1: cl_str = "building" bbox = [max(0, min(255, x)) for x in bbox[0:4]] y = ["{}.jpg".format(tile), width, height, cl_str, bbox[0], bbox[1], bbox[2], bbox[3]] tf_tiles_info.append(y) - #train_len = 0.8 - split_index = int(len(tf_tiles_info) *0.8 ) + + split_index = int(len(tf_tiles_info) * 0.8) column_name = ['filename', 'width', 'height', 'class', 'xmin', 'ymin', 'xmax', 'ymax'] df = pd.DataFrame(tf_tiles_info, columns=column_name) - #shuffle the dataframe + # shuffle the dataframe df = df.sample(frac=1) train_df = df[:split_index] test_df = df[split_index:] print("You have {} training tiles and {} test tiles ready".format( - len(set(train_df['filename'])), len(set(test_df['filename'])))) - # train_df.to_csv("train_df.csv") - # test_df.to_csv("test_df.csv") + len(set(train_df['filename'])), len(set(test_df['filename'])))) tiles_dir = op.join(os.getcwd(), 'tiles') train_dir = op.join(os.getcwd(), 'images', 'train') @@ -152,7 +146,7 @@ def main(_): tf_example = create_tf_example(group, train_dir) writer.write(tf_example.SerializeToString()) writer.close() - output_train= op.join(os.getcwd(),FLAGS.train_rd_path) + output_train = op.join(os.getcwd(), FLAGS.train_rd_path) print('Successfully created the TFRecords: {}'.format(output_train)) ### for test @@ -163,13 +157,13 @@ def main(_): writer.write(tf_example.SerializeToString()) writer.close() - output_test = op.join(os.getcwd(),FLAGS.test_rd_path) + output_test = op.join(os.getcwd(), FLAGS.test_rd_path) print('Successfully created the TFRecords: {}'.format(output_test)) def _score_converter_fn_with_logit_scale(tf_score_converter_fn, logit_scale): def score_converter_fn(logits): cr = logit_scale - cr = tf.constant([[cr]],tf.float32) + cr = tf.constant([[cr]], tf.float32) print(logit_scale) print(logits) scaled_logits = tf.divide(logits, cr, name='scale_logits') #change logit_scale @@ -177,6 +171,5 @@ def score_converter_fn(logits): score_converter_fn.__name__ = '%s_with_logit_scale' % (tf_score_converter_fn.__name__) return score_converter_fn - if __name__ == '__main__': tf.app.run() From f0da1607ac56d98feae1ddca529892ccc4463125 Mon Sep 17 00:00:00 2001 From: Zhuangfang Yi Date: Tue, 30 Jan 2018 17:14:15 -0500 Subject: [PATCH 20/20] pandas --- examples/walkthrough-tensorflow-object-detection.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/walkthrough-tensorflow-object-detection.md b/examples/walkthrough-tensorflow-object-detection.md index ba89a5b..e8921a1 100644 --- a/examples/walkthrough-tensorflow-object-detection.md +++ b/examples/walkthrough-tensorflow-object-detection.md @@ -6,7 +6,7 @@ Google TensorFlow Object Detection API is an open source framework built on top

-First install Label Maker (`pip install label-maker`) and [tippecanoe](https://github.com/mapbox/tippecanoe) +First install Label Maker (`pip install label-maker`), [tippecanoe](https://github.com/mapbox/tippecanoe) and Pandas (`pip install pandas`). ## Create the training dataset