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object_detection_multithreading.py
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object_detection_multithreading.py
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import os
import cv2
import time
import argparse
import numpy as np
import subprocess as sp
import json
import tensorflow as tf
from queue import Queue
from threading import Thread
from utils.app_utils import FPS, HLSVideoStream, WebcamVideoStream, draw_boxes_and_labels
from object_detection.utils import label_map_util
CWD_PATH = os.getcwd()
# Path to frozen detection graph. This is the actual model that is used for the object detection.
MODEL_NAME = 'ssd_mobilenet_v1_coco_11_06_2017'
PATH_TO_CKPT = os.path.join(CWD_PATH, 'object_detection', MODEL_NAME, 'frozen_inference_graph.pb')
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join(CWD_PATH, 'object_detection', 'data', 'mscoco_label_map.pbtxt')
NUM_CLASSES = 90
# 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)
def detect_objects(image_np, sess, detection_graph):
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
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})
# Visualization of the results of a detection.
rect_points, class_names, class_colors = draw_boxes_and_labels(
boxes=np.squeeze(boxes),
classes=np.squeeze(classes).astype(np.int32),
scores=np.squeeze(scores),
category_index=category_index,
min_score_thresh=.5
)
return dict(rect_points=rect_points, class_names=class_names, class_colors=class_colors)
def worker(input_q, output_q):
# Load a (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='')
sess = tf.Session(graph=detection_graph)
fps = FPS().start()
while True:
fps.update()
frame = input_q.get()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
output_q.put(detect_objects(frame_rgb, sess, detection_graph))
fps.stop()
sess.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('-strin', '--stream-input', dest="stream_in", action='store', type=str, default=None)
parser.add_argument('-src', '--source', dest='video_source', type=int,
default=0, help='Device index of the camera.')
parser.add_argument('-wd', '--width', dest='width', type=int,
default=640, help='Width of the frames in the video stream.')
parser.add_argument('-ht', '--height', dest='height', type=int,
default=480, help='Height of the frames in the video stream.')
parser.add_argument('-strout','--stream-output', dest="stream_out", help='The URL to send the livestreamed object detection to.')
args = parser.parse_args()
input_q = Queue(1) # fps is better if queue is higher but then more lags
output_q = Queue()
for i in range(1):
t = Thread(target=worker, args=(input_q, output_q))
t.daemon = True
t.start()
if (args.stream_in):
print('Reading from hls stream.')
video_capture = HLSVideoStream(src=args.stream_in).start()
else:
print('Reading from webcam.')
video_capture = WebcamVideoStream(src=args.video_source,
width=args.width,
height=args.height).start()
fps = FPS().start()
while True:
frame = video_capture.read()
input_q.put(frame)
t = time.time()
if output_q.empty():
pass # fill up queue
else:
font = cv2.FONT_HERSHEY_SIMPLEX
data = output_q.get()
rec_points = data['rect_points']
class_names = data['class_names']
class_colors = data['class_colors']
for point, name, color in zip(rec_points, class_names, class_colors):
cv2.rectangle(frame, (int(point['xmin'] * args.width), int(point['ymin'] * args.height)),
(int(point['xmax'] * args.width), int(point['ymax'] * args.height)), color, 3)
cv2.rectangle(frame, (int(point['xmin'] * args.width), int(point['ymin'] * args.height)),
(int(point['xmin'] * args.width) + len(name[0]) * 6,
int(point['ymin'] * args.height) - 10), color, -1, cv2.LINE_AA)
cv2.putText(frame, name[0], (int(point['xmin'] * args.width), int(point['ymin'] * args.height)), font,
0.3, (0, 0, 0), 1)
if args.stream_out:
print('Streaming elsewhere!')
else:
cv2.imshow('Video', frame)
fps.update()
print('[INFO] elapsed time: {:.2f}'.format(time.time() - t))
if cv2.waitKey(1) & 0xFF == ord('q'):
break
fps.stop()
print('[INFO] elapsed time (total): {:.2f}'.format(fps.elapsed()))
print('[INFO] approx. FPS: {:.2f}'.format(fps.fps()))
video_capture.stop()
cv2.destroyAllWindows()