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import numpy as np
import os
import six.moves.urllib as urllib
import sys
import tensorflow as tf
import collections
import statistics
import math
import tarfile
import os.path
from threading import Lock, Thread
from time import sleep
import cv2
# ZED imports
import pyzed.sl as sl
sys.path.append('utils')
# ## Object detection imports
from object_detection.utils import ops as utils_ops
from object_detection.utils import label_map_util
from object_detection.utils import visualization_utils as vis_util
def load_image_into_numpy_array(image):
ar = image.get_data()
ar = ar[:, :, 0:3]
(im_height, im_width, channels) = image.get_data().shape
return np.array(ar).reshape((im_height, im_width, 3)).astype(np.uint8)
def load_depth_into_numpy_array(depth):
ar = depth.get_data()
ar = ar[:, :, 0:4]
(im_height, im_width, channels) = depth.get_data().shape
return np.array(ar).reshape((im_height, im_width, channels)).astype(np.float32)
lock = Lock()
width = 704
height = 416
confidence = 0.35
image_np_global = np.zeros([width, height, 3], dtype=np.uint8)
depth_np_global = np.zeros([width, height, 4], dtype=np.float)
exit_signal = False
new_data = False
# ZED image capture thread function
def capture_thread_func(svo_filepath=None):
global image_np_global, depth_np_global, exit_signal, new_data
zed = sl.Camera()
# Create a InitParameters object and set configuration parameters
init_params = sl.InitParameters()
init_params.camera_resolution = sl.RESOLUTION.RESOLUTION_HD720
init_params.camera_fps = 30
init_params.depth_mode = sl.DEPTH_MODE.DEPTH_MODE_PERFORMANCE
init_params.coordinate_units = sl.UNIT.UNIT_METER
init_params.svo_real_time_mode = False
if svo_filepath is not None:
init_params.svo_input_filename = svo_filepath
# Open the camera
err = zed.open(init_params)
print(err)
while err != sl.ERROR_CODE.SUCCESS:
err = zed.open(init_params)
print(err)
sleep(1)
image_mat = sl.Mat()
depth_mat = sl.Mat()
runtime_parameters = sl.RuntimeParameters()
while not exit_signal:
if zed.grab(runtime_parameters) == sl.ERROR_CODE.SUCCESS:
zed.retrieve_image(image_mat, sl.VIEW.VIEW_LEFT, width=width, height=height)
zed.retrieve_measure(depth_mat, sl.MEASURE.MEASURE_XYZRGBA, width=width, height=height)
lock.acquire()
image_np_global = load_image_into_numpy_array(image_mat)
depth_np_global = load_depth_into_numpy_array(depth_mat)
new_data = True
lock.release()
sleep(0.01)
zed.close()
def display_objects_distances(image_np, depth_np, num_detections, boxes_, classes_, scores_, category_index):
box_to_display_str_map = collections.defaultdict(list)
box_to_color_map = collections.defaultdict(str)
research_distance_box = 30
for i in range(num_detections):
if scores_[i] > confidence:
box = tuple(boxes_[i].tolist())
if classes_[i] in category_index.keys():
class_name = category_index[classes_[i]]['name']
display_str = str(class_name)
if not display_str:
display_str = '{}%'.format(int(100 * scores_[i]))
else:
display_str = '{}: {}%'.format(display_str, int(100 * scores_[i]))
# Find object distance
ymin, xmin, ymax, xmax = box
x_center = int(xmin * width + (xmax - xmin) * width * 0.5)
y_center = int(ymin * height + (ymax - ymin) * height * 0.5)
x_vect = []
y_vect = []
z_vect = []
min_y_r = max(int(ymin * height), int(y_center - research_distance_box))
min_x_r = max(int(xmin * width), int(x_center - research_distance_box))
max_y_r = min(int(ymax * height), int(y_center + research_distance_box))
max_x_r = min(int(xmax * width), int(x_center + research_distance_box))
if min_y_r < 0: min_y_r = 0
if min_x_r < 0: min_x_r = 0
if max_y_r > height: max_y_r = height
if max_x_r > width: max_x_r = width
for j_ in range(min_y_r, max_y_r):
for i_ in range(min_x_r, max_x_r):
z = depth_np[j_, i_, 2]
if not np.isnan(z) and not np.isinf(z):
x_vect.append(depth_np[j_, i_, 0])
y_vect.append(depth_np[j_, i_, 1])
z_vect.append(z)
if len(x_vect) > 0:
x = statistics.median(x_vect)
y = statistics.median(y_vect)
z = statistics.median(z_vect)
distance = math.sqrt(x * x + y * y + z * z)
display_str = display_str + " " + str('% 6.2f' % distance) + " m "
box_to_display_str_map[box].append(display_str)
box_to_color_map[box] = vis_util.STANDARD_COLORS[classes_[i] % len(vis_util.STANDARD_COLORS)]
for box, color in box_to_color_map.items():
ymin, xmin, ymax, xmax = box
vis_util.draw_bounding_box_on_image_array(
image_np,
ymin,
xmin,
ymax,
xmax,
color=color,
thickness=4,
display_str_list=box_to_display_str_map[box],
use_normalized_coordinates=True)
return image_np
def main(args):
svo_filepath = None
if len(args) > 1:
svo_filepath = args[1]
# This main thread will run the object detection, the capture thread is loaded later
# What model to download and load
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2018_01_28'
MODEL_NAME = 'ssd_mobilenet_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03'
#MODEL_NAME = 'ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03'
#MODEL_NAME = 'ssd_mobilenet_v1_coco_2018_01_28'
#MODEL_NAME = 'faster_rcnn_nas_coco_2018_01_28' # Accurate but heavy
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = 'data/' + MODEL_NAME + '/frozen_inference_graph.pb'
# Check if the model is already present
if not os.path.isfile(PATH_TO_FROZEN_GRAPH):
print("Downloading model " + MODEL_NAME + "...")
MODEL_FILE = MODEL_NAME + '.tar.gz'
MODEL_PATH = 'data/' + MODEL_NAME + '.tar.gz'
DOWNLOAD_BASE = 'http://download.tensorflow.org/models/object_detection/'
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_PATH)
tar_file = tarfile.open(MODEL_PATH)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, 'data/')
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
# Start the capture thread with the ZED input
print("Starting the ZED")
capture_thread = Thread(target=capture_thread_func, kwargs={'svo_filepath': svo_filepath})
capture_thread.start()
# Shared resources
global image_np_global, depth_np_global, new_data, exit_signal
# Load a (frozen) Tensorflow model into memory.
print("Loading model " + MODEL_NAME)
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# Limit to a maximum of 50% the GPU memory usage taken by TF https://www.tensorflow.org/guide/using_gpu
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.5
# Loading label map
label_map = label_map_util.load_labelmap(PATH_TO_LABELS)
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)
# Detection
with detection_graph.as_default():
with tf.Session(config=config, graph=detection_graph) as sess:
while not exit_signal:
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
if new_data:
lock.acquire()
image_np = np.copy(image_np_global)
depth_np = np.copy(depth_np_global)
new_data = False
lock.release()
image_np_expanded = np.expand_dims(image_np, axis=0)
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.
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.
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})
num_detections_ = num_detections.astype(int)[0]
# Visualization of the results of a detection.
image_np = display_objects_distances(
image_np,
depth_np,
num_detections_,
np.squeeze(boxes),
np.squeeze(classes).astype(np.int32),
np.squeeze(scores),
category_index)
cv2.imshow('ZED object detection', cv2.resize(image_np, (width, height)))
if cv2.waitKey(10) & 0xFF == ord('q'):
cv2.destroyAllWindows()
exit_signal = True
else:
sleep(0.01)
sess.close()
exit_signal = True
capture_thread.join()
if __name__ == '__main__':
main(sys.argv)
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