def infer_yolo(timestamp, margin, points):
while True:
yolo_input.wait()
ret, image = yolo_input.pull(flush=True)
if not ret:
continue
# ret, flag = zone_image_update.pull()
# if ret and flag:
# cv2.imwrite('../../../Angular-Dashboard-master/src/assets/rack_image.jpg', image)
# print("After Update")
inference = Inference(image)
img_shape = image.shape
ret, point_set_dict = point_set.pull()
if ret:
point_set_dict["point_set_2"] = [
[0,
0.6 * margin], [img_shape[1] - 4.25 * margin, 0.6 * margin],
[img_shape[1] - 4.25 * margin, img_shape[0] - 0.6 * margin],
[0, img_shape[0] - 0.6 * margin]
]
print("updated points")
points = point_set_dict['point_set_2']
retail_an_object.rack_dict = point_set_dict
retail_an_object.global_init()
inference.get_meta_dict()['warp_points'] = retail_an_object.rack_dict
yolo_ip.push(inference)
yolo_op.wait()
ret, inference = yolo_op.pull()
ret1, zones = zone_detection_in_pipe.pull()
if ret:
i_dets = inference.get_result()
boxes = tracker_stock.update(i_dets)
image = i_dets.get_annotated(boxes)
if ret1 and not zones:
image = retail_an_object.print_shelfNo(image)
image = retail_an_object.misplacedBoxes(boxes, image)
image = retail_an_object.draw_empty_space(boxes, image)
flag = retail_an_object.change_of_state()
# cv2.rectangle(image, (margin, margin), (img_shape[1]-margin, img_shape[0]-margin), (0, 0, 255), 3)
current_time = time.time()
elapsed_seconds = (current_time - timestamp)
# print(elapsed_seconds)
if (elapsed_seconds > 3):
if flag == 1:
# pass
Thread(target=retail_an_object.postdata).start()
timestamp = time.time()
# print("left corner: ",points[0][1])
image = image[int(points[0][1] - margin / 2):int(points[2][1] +
margin / 2),
int(points[0][0]):int(points[2][0] + margin / 2), :]
stock_in_pipe.push(image)
cv2.imshow("retail_out", image)
cv2.waitKey(1)
def read():
while True:
ret, image = cap.read()
if not ret:
continue
image = image.copy()
yolo_input.push(image)
# print("Image read count: ", count)
# count+=1
# image = cv2.resize(image, (int(image.shape[1] / 2), int(image.shape[0] / 2)))
detector_ip.push(Inference(image))
detector_op.wait()
ret, inference = detector_op.pull()
if ret:
i_dets = inference.get_result()
infer_idets = Inference(i_dets)
infer_idets.get_meta_dict()['zone_pipe'] = zone_pipe
trk_ip.push(infer_idets)