cv2.imshow("4", img)
retval, img = camera5.read()
if retval:
cv2.imshow("5", img)
retval, img = camera6.read()
if retval:
cv2.imshow("6", img)
cv2.waitKey(1)
if findCamera:
findCam()
start_time = time.time()
detector = VideoObjectDetection()
defmodel()
custom = detector.CustomObjects(boat=True)
video_path = detector.detectCustomObjectsFromVideo(custom_objects=custom,
input_file_path=os.path.join(execution_path,
inVid),
output_file_path=os.path.join(execution_path, "video_out")
, per_frame_function=forFrame, save_detected_video=True,
minimum_percentage_probability=10, log_progress=True)
print(len(boatList))
print("--- %s seconds ---" % (time.time() - start_time))
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
test_simple.test_simple_inputs(video_path + pictureName + '.jpg',
'mono_1024x320',
execution_path + '\\assets\\proccessed\\',
False)
average_time = datetime.now() - current + average_time
current = datetime.now()
#detections = detector.detectObjectsFromImage(input_image=os.path.join(video_path + pictureName + ".jpg"), output_image_path=os.path.join(execution_path + '\\assets\\proccessed',"new%d.jpg" % count))
detections = detector.detectCustomObjectsFromVideo(
custom_objects=custom_objects,
input_image=os.path.join(video_path + pictureName + ".jpg"),
output_image_path=os.path.join(execution_path + '\\assets\\proccessed',
"new%d.jpg" % count))
current = datetime.now()
average_time = datetime.now() - current + average_time
if (not count == 1):
average_time /= 2
print('ETA is ' + str(average_time * amount_pictures))
numpy_Pic = numpy.load(execution_path + '\\assets\\proccessed\\' +
pictureName + '_disp.npy')
numpy_PicFlat = numpy.ones((numpy_Pic.size, 1))
nPicReal = imread(video_path + pictureName + '.jpg')
