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kuhung committed Dec 8, 2017
0 parents commit ab9daeb84454f36c579b82f13c2c3f66f6b0b8e3
21 LICENSE
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MIT License

Copyright (c) 2016 Andrey Rykov

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
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[![license](https://img.shields.io/github/license/mashape/apistatus.svg)](LICENSE)

## A port of SSD: Single Shot MultiBox Detector to Keras framework.
Refer to [arXiv paper](http://arxiv.org/abs/1512.02325).

- For forward pass for 300x300 model, please, follow `SSD.ipynb` for examples.
- For training procedure for 300x300 model, please, follow `SSD_training.ipynb` for examples.
- Moreover, in `testing_utils` folder there is a useful script to test `SSD` on video or on camera input.

---
- Weights are ported from the original models and are available [here](https://mega.nz/#F!7RowVLCL!q3cEVRK9jyOSB9el3SssIA). You need `weights_SSD300.hdf5`, `weights_300x300_old.hdf5` is for the old version of architecture with 3x3 convolution for `pool6`.


- Weights for chinese [Evernote link](https://app.yinxiang.com/shard/s51/nl/10565191/1944fa71-d815-46b3-ac3b-56ca58ca5b47?title=weights_SSD300.hdf5)


This code was tested with `Keras` v1.2.2, `Tensorflow` v1.0.0, `OpenCV` v3.1.0-dev

386 SSD.ipynb

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import cv2
import keras
from keras.applications.imagenet_utils import preprocess_input
from keras.backend.tensorflow_backend import set_session
from keras.models import Model
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
import tensorflow as tf

import sys

from ssd import SSD300
from ssd_utils import BBoxUtility

plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['image.interpolation'] = 'nearest'

np.set_printoptions(suppress=True)

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.8
set_session(tf.Session(config=config))

voc_classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',
'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',
'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',
'Sheep', 'Sofa', 'Train', 'Tvmonitor']
NUM_CLASSES = len(voc_classes) + 1

input_shape=(300, 300, 3)
model = SSD300(input_shape, num_classes=NUM_CLASSES)
model.load_weights('weights_SSD300.hdf5', by_name=True)
bbox_util = BBoxUtility(NUM_CLASSES)

from PIL import Image

def get_rectangle(img_file,img_name,target_file,target_label):

inputs = []
images = []
img_path = '{}/{}.jpg'.format(img_file,img_name)
im = Image.open(img_path)
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
inputs = preprocess_input(np.array(inputs))

preds = model.predict(inputs, batch_size=1, verbose=1)
results = bbox_util.detection_out(preds)

for i, img in enumerate(images):
det_label = results[i][:, 0]
det_conf = results[i][:, 1]
det_xmin = results[i][:, 2]
det_ymin = results[i][:, 3]
det_xmax = results[i][:, 4]
det_ymax = results[i][:, 5]

top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]

top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin = det_xmin[top_indices]
top_ymin = det_ymin[top_indices]
top_xmax = det_xmax[top_indices]
top_ymax = det_ymax[top_indices]

for i in range(top_conf.shape[0]):
xmin = int(round(top_xmin[i] * img.shape[1]))
ymin = int(round(top_ymin[i] * img.shape[0]))
xmax = int(round(top_xmax[i] * img.shape[1]))
ymax = int(round(top_ymax[i] * img.shape[0]))

label = int(top_label_indices[i])
label_name = voc_classes[label - 1]

if label_name=="Person":
region = im.crop((xmin, ymin, xmax, ymax))
region.save('{}/{}.jpg'.format(target_file,img_name))

import os
import sys
from tqdm import *
target_label=sys.argv[1]
img_file=sys.argv[2]
target_file=sys.argv[3]

if os.path.exists(target_file):
pass
else:
os.mkdir(target_file)

files = os.listdir(img_file)
for file in tqdm(files):
if 'jpg' in file:
img_name=file[:-4]
get_rectangle(img_file,img_name,target_file,target_label)

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import cv2
import keras
from keras.applications.imagenet_utils import preprocess_input
from keras.backend.tensorflow_backend import set_session
from keras.models import Model
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
import tensorflow as tf

import sys

from ssd import SSD300
from ssd_utils import BBoxUtility

plt.rcParams['figure.figsize'] = (8, 8)
plt.rcParams['image.interpolation'] = 'nearest'

np.set_printoptions(suppress=True)

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.8
set_session(tf.Session(config=config))

voc_classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',
'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',
'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',
'Sheep', 'Sofa', 'Train', 'Tvmonitor']
NUM_CLASSES = len(voc_classes) + 1

input_shape=(300, 300, 3)
model = SSD300(input_shape, num_classes=NUM_CLASSES)
model.load_weights('weights_SSD300.hdf5', by_name=True)
bbox_util = BBoxUtility(NUM_CLASSES)

from PIL import Image

def get_rectangle(img_file,img_name,target_file,target_label):

inputs = []
images = []
img_path = '{}/{}.jpg'.format(img_file,img_name)
im = Image.open(img_path)
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
inputs = preprocess_input(np.array(inputs))

preds = model.predict(inputs, batch_size=1, verbose=1)
results = bbox_util.detection_out(preds)

for i, img in enumerate(images):
det_label = results[i][:, 0]
det_conf = results[i][:, 1]
det_xmin = results[i][:, 2]
det_ymin = results[i][:, 3]
det_xmax = results[i][:, 4]
det_ymax = results[i][:, 5]

top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]

top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin = det_xmin[top_indices]
top_ymin = det_ymin[top_indices]
top_xmax = det_xmax[top_indices]
top_ymax = det_ymax[top_indices]

for i in range(top_conf.shape[0]):
xmin = int(round(top_xmin[i] * img.shape[1]))
ymin = int(round(top_ymin[i] * img.shape[0]))
xmax = int(round(top_xmax[i] * img.shape[1]))
ymax = int(round(top_ymax[i] * img.shape[0]))

label = int(top_label_indices[i])
label_name = voc_classes[label - 1]

if label_name==target_label:
region = im.crop((xmin, ymin, xmax, ymax))
region.save('{}/{}.jpg'.format(target_file,img_name))

import os
import sys
from tqdm import *
target_label=sys.argv[1]
img_file=sys.argv[2]
target_file=sys.argv[3]

if os.path.exists(target_file):
pass
else:
os.mkdir(target_file)

files = os.listdir(img_file)
for file in tqdm(files):
if 'jpg' in file:
img_name=file[:-4]
get_rectangle(img_file,img_name,target_file,target_label)

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