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app.py
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app.py
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## Author: Victor Dibia
## Load hand tracking model, spin up web socket and web application.
from utils import detector_utils as detector_utils
from utils import object_id_utils as id_utils
import cv2
import tensorflow as tf
import multiprocessing
from multiprocessing import Queue, Pool
from utils.detector_utils import WebcamVideoStream
import time
import datetime
import argparse
from threading import Thread
from flask import Flask, render_template, request, jsonify
from utils import web_socket_server
from utils import web_socket_client
frame_processed = 0
score_thresh = 0.7
num_hands_detect = 10
num_classes = 1
web_socket_port = 5006
web_socket_client_url = "ws://localhost:5006"
web_socket_server.init(web_socket_port)
# Initialize websocket client for sending messages
web_socket_client.socket_init(web_socket_client_url)
# Create a worker thread that loads graph and
# does detection on images in an input queue and puts it on an output queue
label_path = "hand_inference_graph/hand_label_map.pbtxt"
frozen_graph_path = "hand_inference_graph/frozen_inference_graph.pb"
object_refresh_timeout = 3
seen_object_list = {}
# Set up web application serving
app = Flask(__name__, )
@app.route("/")
def hello():
return render_template('mousecontrol.html')
@app.route("/hand")
def test():
return render_template('handcontrol.html')
# Worker threads that process video frame
def worker(input_q, output_q, cap_params, frame_processed):
print(">> loading frozen model for worker")
detection_graph, sess, category_index = detector_utils.load_inference_graph(num_classes, frozen_graph_path, label_path)
sess = tf.Session(graph=detection_graph)
while True:
#print("> ===== in worker loop, frame ", frame_processed)
frame = input_q.get()
if (frame is not None):
# actual detection
boxes, scores, classes = detector_utils.detect_objects(
frame, detection_graph, sess)
tags = detector_utils.get_tags(classes, category_index, num_hands_detect, score_thresh, scores, boxes, frame)
if (len(tags) > 0):
id_utils.get_id(tags, seen_object_list)
web_socket_client.send_message(tags,"hand")
id_utils.refresh_seen_object_list(seen_object_list, object_refresh_timeout)
detector_utils.draw_box_on_image_id(tags, frame)
output_q.put(frame)
frame_processed += 1
else:
output_q.put(frame)
sess.close()
def launch_webserver():
app.config['APPLICATION_ROOT'] = "/static"
app.run(host='0.0.0.0', port=5005)
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=2, help='Max number of hands to detect.')
parser.add_argument('-fps', '--fps', dest='fps', type=int,
default=0, help='Show FPS on detection/display visualization')
parser.add_argument('-wd', '--width', dest='width', type=int,
default=200, help='Width of the frames in the video stream.')
parser.add_argument('-ht', '--height', dest='height', type=int,
default=180, 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=2, 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_device_id = 0
video_capture = WebcamVideoStream(src=video_device_id,
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
# run web application
Thread(target=launch_webserver).start()
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
# print("frame ", index, num_frames, elapsed_time, fps)
cv2.namedWindow("Hand Tracking",cv2.WINDOW_NORMAL)
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('Hand Tracking', 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:
# print("video end")
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()