def main():
"""Main function for the program. Everything starts here.
:return: None
"""
global resize_output, resize_output_width, resize_output_height, \
obj_detector_proc, resize_output, resize_output_width, resize_output_height, video_proc, \
last_num_persons, asyncImWriter
if (not handle_args()):
print_usage()
return 1
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) < 1:
print('No NCS device detected.')
print('Insert device and try again!')
return 1
# Pick the first stick to run the network
# use the first NCS device that opens for the object detection.
dev_count = 0
for one_device in devices:
try:
obj_detect_dev = mvnc.Device(one_device)
obj_detect_dev.open()
print("opened device " + str(dev_count))
break
except:
print("Could not open device " + str(dev_count) +
", trying next device")
pass
dev_count += 1
cv2.namedWindow(cv_window_name)
cv2.moveWindow(cv_window_name, 10, 10)
cv2.waitKey(1)
obj_detector_proc = SsdMobileNetProcessor(
NETWORK_GRAPH_FILENAME,
obj_detect_dev,
inital_box_prob_thresh=min_score_percent / 100.0,
classification_mask=object_classifications_mask)
exit_app = False
while (True):
# video processor that will put video frames images on the object detector's input FIFO queue
video_proc = CameraProcessor(CAMERA_INDEX,
1920,
1080,
network_processor=obj_detector_proc)
video_proc.start_processing()
frame_count = 0
start_time = time.time()
end_time = start_time
while (True):
try:
(filtered_objs, display_image
) = obj_detector_proc.get_async_inference_result()
except:
print("exception caught in main")
raise
# check if the window is visible, this means the user hasn't closed
# the window via the X button
prop_val = cv2.getWindowProperty(cv_window_name,
cv2.WND_PROP_ASPECT_RATIO)
if (prop_val < 0.0):
end_time = time.time()
video_proc.stop_processing()
exit_app = True
break
agg_results = overlay_on_image(display_image, filtered_objs)
num_persons = len(agg_results)
if (show_output):
if (resize_output):
display_image = cv2.resize(
display_image,
(resize_output_width, resize_output_height),
cv2.INTER_LINEAR)
cv2.imshow(cv_window_name, display_image)
raw_key = cv2.waitKey(1)
if (raw_key != -1):
if (handle_keys(raw_key, obj_detector_proc) == False):
end_time = time.time()
exit_app = True
video_proc.stop_processing()
break
frame_count += 1
# if (obj_detector_proc.is_input_queue_empty()):
# end_time = time.time()
# print('Neural Network Processor has nothing to process, assuming video is finished.')
# break
frames_per_second = frame_count / (end_time - start_time)
print('Frames per Second: ' + str(frames_per_second))
throttling = obj_detect_dev.get_option(
mvnc.DeviceOption.RO_THERMAL_THROTTLING_LEVEL)
if (throttling > 0):
print("\nDevice is throttling, level is: " + str(throttling))
print("Sleeping for a few seconds....")
cv2.waitKey(2000)
#video_proc.stop_processing()
cv2.waitKey(1)
if (exit_app):
video_proc.cleanup()
break
# Clean up the graph and the device
obj_detector_proc.cleanup()
obj_detect_dev.close()
obj_detect_dev.destroy()
cv2.destroyAllWindows()
def main():
"""Main function for the program. Everything starts here.
:return: None
"""
global resize_output, resize_output_width, resize_output_height, \
obj_detector_proc, resize_output, resize_output_width, resize_output_height, video_proc
if (not handle_args()):
print_usage()
return 1
# get list of all the .mp4 files in the image directory
# input_video_filename_list = os.listdir(input_video_path)
# input_video_filename_list = [i for i in input_video_filename_list if i.endswith('.mp4')]
# if (len(input_video_filename_list) < 1):
# # no images to show
# print('No video (.mp4) files found')
# return 1
# Set logging level to only log errors
mvnc.global_set_option(mvnc.GlobalOption.RW_LOG_LEVEL, 3)
devices = mvnc.enumerate_devices()
if len(devices) < 1:
print('No NCS device detected.')
print('Insert device and try again!')
return 1
# Pick the first stick to run the network
# use the first NCS device that opens for the object detection.
# open as many devices as we find
dev_count = 0
ncs_devices = []
obj_detectors = []
# open as many devices as detected
for one_device in devices:
print('one device ', one_device, 'dev_count ', dev_count, 'devices ',
devices)
obj_detect_dev = mvnc.Device(one_device)
status = obj_detect_dev.open()
ncs_devices.append(obj_detect_dev)
obj_detector_proc = SsdMobileNetProcessor(
NETWORK_GRAPH_FILENAME,
ncs_devices, # obj_detect_dev,
inital_box_prob_thresh=min_score_percent / 100.0,
classification_mask=object_classifications_mask)
obj_detectors.append(obj_detector_proc)
print("opened device " + str(dev_count), 'status ', status)
dev_count += 1
print('ncs_devices', ncs_devices)
cv2.namedWindow(cv_window_name)
cv2.moveWindow(cv_window_name, 10, 10)
cv2.waitKey(1)
#obj_detector_proc = SsdMobileNetProcessor(NETWORK_GRAPH_FILENAME, ncs_devices[0], # obj_detect_dev,
# inital_box_prob_thresh=min_score_percent/100.0,
# classification_mask=object_classifications_mask)
exit_app = False
# output file
# fourcc = cv2.VideoWriter_fourcc(*"MJPG")
fourcc = cv2.VideoWriter_fourcc(*"mp4v")
filenum = 1
# keep the number of video files to a reasonable number in the current directory
while (filenum < 20):
doesexist = os.path.isfile("output" + str(filenum) + ".mp4")
if (doesexist == False):
out_filename = "output" + str(filenum) + ".mp4"
break
filenum += 1
print("Using output file name " + out_filename)
outfile = cv2.VideoWriter(out_filename, fourcc, 11.0, (640, 480))
while (True):
# for input_video_file in input_video_filename_list :
# video processor that will put video frames images on the object detector's input FIFO queue
# video_proc = VideoProcessor(input_video_path + '/' + input_video_file,
# network_processor = obj_detector_proc)
# use the video cam (0) ***swb***
video_proc = VideoProcessor(
0, network_processor=obj_detectors[0]) # obj_detector_proc)
video_proc.start_processing()
frame_count = 0
start_time = time.time()
end_time = start_time
while (True):
try:
(filtered_objs, display_image
) = obj_detector_proc.get_async_inference_result()
except:
print("exception caught in main")
raise
# check if the window is visible, this means the user hasn't closed
# the window via the X button
prop_val = cv2.getWindowProperty(cv_window_name,
cv2.WND_PROP_ASPECT_RATIO)
if (prop_val < 0.0):
end_time = time.time()
video_proc.stop_processing()
exit_app = True
break
overlay_on_image(display_image, filtered_objs)
if (resize_output):
display_image = cv2.resize(
display_image, (resize_output_width, resize_output_height),
cv2.INTER_LINEAR)
cv2.imshow(cv_window_name, display_image)
outfile.write(display_image)
raw_key = cv2.waitKey(1)
if (raw_key != -1):
if (handle_keys(raw_key, obj_detector_proc) == False):
end_time = time.time()
exit_app = True
video_proc.stop_processing()
continue
frame_count += 1
if (obj_detector_proc.is_input_queue_empty()):
end_time = time.time()
print(
'Neural Network Processor has nothing to process, assuming video is finished.'
)
break
frames_per_second = frame_count / (end_time - start_time)
print('Frames per Second: ' + str(frames_per_second))
throttling = obj_detect_dev.get_option(
mvnc.DeviceOption.RO_THERMAL_THROTTLING_LEVEL)
if (throttling > 0):
print("\nDevice is throttling, level is: " + str(throttling))
print("Sleeping for a few seconds....")
cv2.waitKey(2000)
#video_proc.stop_processing()
cv2.waitKey(1)
video_proc.cleanup()
if (exit_app):
break
#if (exit_app):
# break
# Clean up the graph and the device
obj_detector_proc.cleanup()
obj_detect_dev.close()
obj_detect_dev.destroy()
cv2.destroyAllWindows()
outfile.release()