/
multi_threaded_detection.py
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/
multi_threaded_detection.py
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import detector_utils
import cv2
import tensorflow as tf
import multiprocessing
from multiprocessing import Queue, Pool
import time
from detector_utils import WebcamVideoStream
import datetime
import argparse
frame_processed = 0
score_thresh = 0.3
# Creates a worker thread that loads graph and does detection on images in an input queue, then puts it on an output queue
def worker(input_q, output_q, cap_params, frame_processed):
print(">> Loading frozen model for worker.")
detection_graph, sess = detector_utils.load_inference_graph()
sess = tf.compat.v1.Session(graph=detection_graph)
while True:
frame = input_q.get()
if (frame is not None):
'''Boxes contain coordinates for detected hands
Scores contains condfidence levels
If len(boxes) > 1, at least one hand is detected
You can change the score_thresh value as desired'''
boxes, scores = detector_utils.detect_objects(
frame, detection_graph, sess)
# Draws bounding boxes
detector_utils.draw_box_on_image(
cap_params['num_hands_detect'], cap_params["score_thresh"],
scores, boxes, cap_params['im_width'], cap_params['im_height'],
frame)
# Adds frame annotated with bounding box to queue
output_q.put(frame)
frame_processed += 1
else:
output_q.put(frame)
sess.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument(
'-src',
'--source',
dest='video_source',
type=int,
default=0,
help='Device index of the camera.')
parser.add_argument(
'-nhands',
'--num_hands',
dest='num_hands',
type=int,
default=1, # Change this for max number of hands to detect
help='Max number of hands to detect.')
parser.add_argument(
'-fps',
'--fps',
dest='fps',
type=int,
default=1,
help='Show FPS on detection/display visualization')
parser.add_argument(
'-wd',
'--width',
dest='width',
type=int,
default=400,
help='Width of the frames in the video stream.')
parser.add_argument(
'-ht',
'--height',
dest='height',
type=int,
default=225,
help='Height of the frames in the video stream.')
parser.add_argument(
'-ds',
'--display',
dest='display',
type=int,
default=1,
help='Display the detected images using OpenCV. This reduces FPS')
parser.add_argument(
'-num-w',
'--num-workers',
dest='num_workers',
type=int,
default=4,
help='Number of workers.')
parser.add_argument(
'-q-size',
'--queue-size',
dest='queue_size',
type=int,
default=5,
help='Size of the queue.')
args = parser.parse_args()
input_q = Queue(maxsize=args.queue_size)
output_q = Queue(maxsize=args.queue_size)
video_capture = WebcamVideoStream(
src=args.video_source, width=args.width, height=args.height).start()
cap_params = {}
frame_processed = 0
cap_params['im_width'], cap_params['im_height'] = video_capture.size()
cap_params['score_thresh'] = score_thresh
# Max number of hands we want to detect/track
cap_params['num_hands_detect'] = args.num_hands
print(cap_params, args)
# Spin up workers to paralleize detection.
pool = Pool(args.num_workers, worker,
(input_q, output_q, cap_params, frame_processed))
start_time = datetime.datetime.now()
num_frames = 0
fps = 0
index = 0
cv2.namedWindow('Multi-Threaded Detection', cv2.WINDOW_NORMAL)
while True:
frame = video_capture.read()
frame = cv2.flip(frame, 1)
index += 1
input_q.put(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
output_frame = output_q.get()
output_frame = cv2.cvtColor(output_frame, cv2.COLOR_RGB2BGR)
elapsed_time = (datetime.datetime.now() - start_time).total_seconds()
num_frames += 1
fps = num_frames / elapsed_time
if (output_frame is not None):
if (args.display > 0):
if (args.fps > 0):
detector_utils.draw_fps_on_image("FPS: " + str(int(fps)),
output_frame)
cv2.imshow('Multi-Threaded Detection', output_frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
if (num_frames == 400):
num_frames = 0
start_time = datetime.datetime.now()
else:
print("frames processed: ", index, "elapsed time: ",
elapsed_time, "fps: ", str(int(fps)))
else:
break
k = cv2.waitKey(25) & 0xFF
if k == 27:
break
elapsed_time = (datetime.datetime.now() - start_time).total_seconds()
fps = num_frames / elapsed_time
print("fps", fps)
pool.terminate()
video_capture.stop()
cv2.destroyAllWindows()