def postProcess(result_list,
width,
height,
labels_map,
out_path,
is_async_mode,
ren_progress_file_path=None):
post_process_t = time.time()
# 0x00000021,
vw = cv2.VideoWriter(out_path, cv2.VideoWriter_fourcc(*"AVC1"), 30.0,
(width, height), True)
for i in range(len(result_list)):
frame, res = result_list[i]
if len(res) > 0:
placeBoxes(res, labels_map, frame, is_async_mode)
vw.write(frame)
if not ren_progress_file_path is None:
progressUpdate(ren_progress_file_path,
time.time() - post_process_t, i + 1,
len(result_list))
vw.release()
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
job_id = str(os.environ['PBS_JOBID'])
job_id = job_id.rstrip().split('.')[0]
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for {} device...".format(args.device))
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
log.info("Loading plugins for {} device...".format(args.device))
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(plugin.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
if args.input == 'cam':
input_stream = 0
out_file_name = 'cam'
else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net,
num_requests=args.number_infer_requests)
log.info(
"Starting inference in async mode, {} requests in parallel...".format(
args.number_infer_requests))
result_file = open(os.path.join(args.output_dir, job_id, 'output.txt'),
"w")
pre_infer_file = os.path.join(args.output_dir, job_id, 'pre_progress.txt')
infer_file = os.path.join(args.output_dir, job_id, 'i_progress.txt')
processed_vid = '/tmp/processed_vid.bin'
# Read and pre-process input image
if isinstance(net.inputs[input_blob], list):
n, c, h, w = net.inputs[input_blob]
else:
n, c, h, w = net.inputs[input_blob].shape
del net
cap = cv2.VideoCapture(input_stream)
video_len = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
if video_len < args.number_infer_requests:
args.number_infer_requests = video_len
#Pre inference processing, read mp4 frame by frame, process using openCV and write to binary file
width = int(cap.get(3))
height = int(cap.get(4))
CHUNKSIZE = n * c * w * h
id_ = 0
with open(processed_vid, 'w+b') as f:
time_start = time.time()
while cap.isOpened():
ret, next_frame = cap.read()
if not ret:
break
in_frame = cv2.resize(next_frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
bin_frame = bytearray(in_frame)
f.write(bin_frame)
id_ += 1
if id_ % 10 == 0:
progressUpdate(pre_infer_file,
time.time() - time_start, id_, video_len)
cap.release()
if args.labels:
with open(args.labels, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
log.info("Starting inference in async mode...")
log.info("To switch between sync and async modes press Tab button")
log.info("To stop the sample execution press Esc button")
current_inference = 0
previous_inference = 1 - args.number_infer_requests
infer_requests = exec_net.requests
frame_count = 0
try:
infer_time_start = time.time()
with open(processed_vid, "rb") as data:
while frame_count < video_len:
# Read next frame from input stream if available and submit it for inference
byte = data.read(CHUNKSIZE)
if not byte == b"":
deserialized_bytes = np.frombuffer(byte, dtype=np.uint8)
in_frame = np.reshape(deserialized_bytes,
newshape=(n, c, h, w))
exec_net.start_async(request_id=current_inference,
inputs={input_blob: in_frame})
# Retrieve the output of an earlier inference request
if previous_inference >= 0:
status = infer_requests[previous_inference].wait()
if status is not 0:
raise Exception(
"Infer request not completed successfully")
# Parse inference results
res = infer_requests[previous_inference].outputs[out_blob]
processBoxes(frame_count, res, labels_map,
args.prob_threshold, width, height,
result_file)
frame_count += 1
# Write data to progress tracker
if frame_count % 10 == 0:
progressUpdate(infer_file,
time.time() - infer_time_start,
frame_count + 1, video_len + 1)
# Increment counter for the inference queue and roll them over if necessary
current_inference += 1
if current_inference >= args.number_infer_requests:
current_inference = 0
previous_inference += 1
if previous_inference >= args.number_infer_requests:
previous_inference = 0
progressUpdate(infer_file,
time.time() - infer_time_start, frame_count + 1,
video_len + 1)
# End while loop
total_time = round(time.time() - infer_time_start, 2)
stats = {}
stats['time'] = str(total_time)
stats['frames'] = str(frame_count)
stats['fps'] = str(round(frame_count / total_time, 2))
with open(os.path.join(args.output_dir, job_id, 'stats.json'),
'w') as json_file:
json.dump(stats, json_file)
result_file.close()
finally:
log.info("Processing done...")
del exec_net
del plugin
def main():
"""
Load the network and parse the SSD output.
:return: None
"""
args = build_argparser().parse_args()
# Flag for the input image
single_image_mode = False
total_count = 0
cur_request_id = 0
last_count = 0
total_count = 0
start_time = 0
# Initialise the class
infer_network = Network()
# Load the network to IE plugin to get shape of input layer
n, c, h, w = infer_network.load_model(args.model, args.device, 1, 1,
cur_request_id, args.cpu_extension)
# Checks for live feed
#if args.input == 'CAM':
#input_stream = 0
# Checks for input image
if args.input.endswith('.jpg') or args.input.endswith('.bmp'):
single_image_mode = True
input_stream = args.input
# Checks for video file
else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
cap = cv2.VideoCapture(input_stream)
video_len = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_count = 0
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
progress_file_path = os.path.join(args.output_dir, str(job_id),
'i_progress.txt')
infer_time_start = time.time()
if input_stream:
cap.open(args.input)
if not cap.isOpened():
log.error("ERROR! Unable to open video source")
global initial_w, initial_h, prob_threshold
prob_threshold = args.prob_threshold
initial_w = cap.get(3)
initial_h = cap.get(4)
people_counter = cv2.VideoWriter(
os.path.join(args.output_dir, str(job_id), "people_counter.mp4"),
cv2.VideoWriter_fourcc(*"AVC1"), fps, (int(initial_w), int(initial_h)),
True)
while cap.isOpened():
flag, frame = cap.read()
frame_count += 1
if not flag:
break
# Start async inference
image = cv2.resize(frame, (w, h))
# Change data layout from HWC to CHW
image = image.transpose((2, 0, 1))
image = image.reshape((n, c, h, w))
# Start asynchronous inference for specified request.
inf_start = time.time()
infer_network.exec_net(cur_request_id, image)
# Wait for the result
if infer_network.wait(cur_request_id) == 0:
det_time = time.time() - inf_start
# Results of the output layer of the network
result = infer_network.get_output(cur_request_id)
if args.perf_counts:
perf_count = infer_network.performance_counter(cur_request_id)
performance_counts(perf_count)
frame, current_count = ssd_out(frame, result)
inf_time_message = "Inference time: {:.3f}ms"\
.format(det_time * 1000)
cv2.putText(frame, inf_time_message, (15, 15),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
current_count_message = "Current count: {}"\
.format(current_count)
cv2.putText(frame, current_count_message, (15, 30),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
last_count = current_count
people_counter.write(frame)
if frame_count % 10 == 0 or frame_count % video_len == 0:
print("frame_count: {}, video_len: {}".format(
frame_count, video_len))
progressUpdate(progress_file_path,
int(time.time() - infer_time_start), frame_count,
video_len)
if single_image_mode:
cv2.imwrite('output_image.jpg', frame)
if args.output_dir:
total_time = round(time.time() - infer_time_start, 2)
stats = {}
stats['time'] = str(total_time)
stats['frames'] = str(frame_count)
stats['fps'] = str(round(frame_count / total_time, 2))
with open(os.path.join(args.output_dir, str(job_id), 'stats.json'),
'w') as f:
json.dump(stats, f)
cap.release()
infer_network.clean()
else:
start_time = time.time()
pred_mask = model.predict(img, verbose=0, steps=None)
#print ("Time for prediction TF: ", '\033[1m %.0f \033[0m'%((time.time()-start_time)*1000),"ms")
end_time = (time.time()-start_time)*1000
print(end_time)
plotDiceScore(img_no,img,msk,pred_mask,plot_result, round(end_time))
return end_time
indicies_validation = [40, 63, 43, 55, 99, 101, 19, 46] #[40]
val_id = 1
infer_time_start = time.time()
progress_file_path = os.path.join(png_directory, "i_progress.txt")
infer_time = 0
for idx in indicies_validation:
infer_time_idx = predict(idx, plot_result=True)
if val_id > 2:
infer_time += infer_time_idx
#print((time.time()-infer_time_start)*1000/val_id)
progressUpdate(progress_file_path, time.time()-infer_time_start, val_id, len(indicies_validation)-1)
val_id += 1
total_time = round(time.time() - infer_time_start, 2)
stats = {}
stats['time'] = str(total_time)
stats['frames'] = str(val_id - 2)
stats['fps'] = str( round((val_id - 2) / total_time , 2))
with open(os.path.join(png_directory, 'stats.json'), 'w') as json_file:
json.dump(stats, json_file)
def main():
colormap = 'viridis'
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
device = args.device
fp16 = True
if device == "CPU":
fp16 = False
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=device)
# Read IR
net = IENetwork(model=model_xml, weights=model_bin)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
bn = "relu_1/Relu"
print(bn)
# add the last convolutional layer as output
net.add_outputs(bn)
fc = "predictions_1/MatMul"
# name of the inputs and outputs
input_blob = next(iter(net.inputs))
out_blob = "predictions_1/Sigmoid"
net.batch_size = 1
exec_net = plugin.load(network=net)
n, c, h, w = net.inputs[input_blob].shape
print("Current Directory:", os.getcwd())
files = glob.glob(os.getcwd() + args.input[0])
if not os.path.isdir(args.output_dir):
os.makedirs(args.output_dir, exist_ok=True)
f = open(os.path.join(args.output_dir, job_id, 'result.txt'), 'w')
progress_file_path = os.path.join(args.output_dir, job_id,
"i_progress.txt")
print(progress_file_path)
time_images = []
tstart = time.time()
for index_f, file in enumerate(files):
[image1, image] = read_image(file)
t0 = time.time()
for i in range(args.number_iter):
res = exec_net.infer(inputs={input_blob: image1})
#infer_time.append((time()-t0)*1000)
infer_time = (time.time() - t0) * 1000
log.info("Average running time of one iteration: {} ms".format(
np.average(np.asarray(infer_time))))
if args.perf_counts:
perf_counts = exec_net.requests[0].get_perf_counts()
log.info("Performance counters:")
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
'name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
for layer, stats in perf_counts.items():
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
layer, stats['layer_type'], stats['exec_type'],
stats['status'], stats['real_time']))
res_pb = res[out_blob]
probs = res_pb[0][0]
print("Probability of having disease= " + str(probs) +
", performed in " + str(np.average(np.asarray(infer_time))) +
" ms")
# Class Activation Map
t0 = time.time()
cam = class_activation_map_openvino(res, bn, fc, net, fp16)
cam_time = (time.time() - t0) * 1000
print("Time for CAM: {} ms".format(cam_time))
fig, ax = plt.subplots(1, 2)
# Visualize the CAM heatmap
cam = (cam - np.min(cam)) / (np.max(cam) - np.min(cam))
im = ax[0].imshow(cam, cmap=colormap)
ax[0].axis('off')
plt.colorbar(im, ax=ax[0], fraction=0.046, pad=0.04)
# Visualize the CAM overlaid over the X-ray image
colormap_val = cm.get_cmap(colormap)
imss = np.uint8(colormap_val(cam) * 255)
im = Image.fromarray(imss)
width, height = image.size
cam1 = resize_image(im, (height, width))
heatmap = np.asarray(cam1)
img1 = heatmap[:, :, :3] * 0.3 + image
ax[1].imshow(np.uint16(img1))
plt.xticks([]), plt.yticks([]) # to hide tick values on X and Y axis
plt.savefig(os.path.join(args.output_dir, job_id,
'result' + str(index_f) + '.png'),
bbox_inches='tight',
pad_inches=0,
dpi=300)
avg_time = round((infer_time / args.number_iter), 1)
#f.write(res + "\n Inference performed in " + str(np.average(np.asarray(infer_time))) + "ms")
f.write("Pneumonia probability: " + str(probs) +
", Inference performed in " + str(avg_time) + "ms \n")
time_images.append(avg_time)
progressUpdate(progress_file_path, index_f * avg_time, index_f + 1,
len(files))
total_time = round(time.time() - tstart, 2)
stats = {}
stats['time'] = str(total_time)
stats['frames'] = str(len(files))
stats['fps'] = str(round(len(files) / total_time, 2))
with open(os.path.join(args.output_dir, job_id, 'stats.json'),
'w') as json_file:
json.dump(stats, json_file)
if not os.path.isdir(result_dir):
print(result_dir)
os.makedirs(result_dir, exist_ok=True)
print(args.input)
input_img = Image.open(args.input[0])
width, height = input_img.size
op_height, op_width = height // 2, width // 2
print(width, height, op_width, op_height)
total_op_imgs = 4
img_num = 0
t0 = time()
for i in range(0, height, op_height):
for j in range(0, width, op_width):
print(i, j)
img_num += 1
qarpo.progressUpdate(progress_file_path,
time() - t0, img_num, total_op_imgs)
box = (j, i, j + op_width, i + op_height)
a = input_img.crop(box)
print("image cropped")
a.save(os.path.join(result_dir, f"IMG_{img_num}.png"))
t1 = time() - t0
stats = {}
stats['time'] = str(round(t1, 1))
stats['frames'] = str(img_num)
stats['fps'] = str(img_num / t1)
stats_file = result_dir + "/stats.json"
with open(stats_file, 'w') as f:
json.dump(stats, f)
def main():
job_id = os.environ['PBS_JOBID']
codec = data_utils.TextFeatureIO(
char_dict_path='app/Config/char_dict.json',
ord_map_dict_path=r'app/Config/ord_map.json')
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
net = IENetwork(model=model_xml, weights=model_bin)
# if plugin.device == "CPU":
# supported_layers = plugin.get_supported_layers(net)
# not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
# if len(not_supported_layers) != 0:
# log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
# format(plugin.device, ', '.join(not_supported_layers)))
# log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
# "or --cpu_extension command line argument")
# sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
log.info("Preparing input blobs")
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
net.batch_size = len(args.input)
job_id = os.environ['PBS_JOBID'].split('.')[0]
# Read and pre-process input images
n, c, h, w = net.inputs[input_blob].shape
images = np.ndarray(shape=(n, c, h, w))
for i in range(n):
image = cv2.imread(args.input[i])
if image.shape[:-1] != (h, w):
log.warning("Image {} is resized from {} to {}".format(
args.input[i], image.shape[:-1], (h, w)))
image = cv2.resize(image, (w, h))
image = image.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
images[i] = image
log.info("Batch size is {}".format(n))
# Loading model to the plugin
log.info("Loading model to the plugin")
exec_net = plugin.load(network=net)
del net
# Start sync inference
log.info("Starting inference ({} iterations)".format(args.number_iter))
infer_time = []
result_dir = os.path.join(args.output_dir, job_id)
if not os.path.isdir(result_dir):
print(result_dir)
os.makedirs(result_dir, exist_ok=True)
progress_file_path = os.path.join(result_dir, 'i_progress.txt')
t0 = time()
for i in range(args.number_iter):
#t0 = time()
res = exec_net.infer(inputs={input_blob: images})
#infer_time.append((time()-t0)*1000)
print(i, args.number_iter)
progressUpdate(progress_file_path,
time() - t0, i + 1, args.number_iter)
t1 = (time() - t0)
log.info("Average running time of one iteration: {} ms".format(
np.average(np.asarray(infer_time))))
if args.perf_counts:
perf_counts = exec_net.requests[0].get_perf_counts()
log.info("Performance counters:")
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
'name', 'layer_type', 'exet_type', 'status', 'real_time, us'))
for layer, stats in perf_counts.items():
print("{:<70} {:<15} {:<15} {:<15} {:<10}".format(
layer, stats['layer_type'], stats['exec_type'],
stats['status'], stats['real_time']))
# Processing output blob
log.info("Processing output blob")
res = res[out_blob]
preds = res.argmax(2)
preds = preds.transpose(1, 0)
preds = np.ascontiguousarray(preds, dtype=np.int8).view(dtype=np.int8)
values = codec.writer.ordtochar(preds[0].tolist())
values = [v for i, v in enumerate(values) if i == 0 or v != values[i - 1]]
values = [x for x in values if x != ' ']
res = ''.join(values)
print("The result is : " + res)
avg_time = round((t1 * 1000 / args.number_iter), 3)
with open(os.path.join(args.output_dir, job_id, 'result.txt'), 'w') as f:
f.write(res + "\n Inference performed in " + str(avg_time) + "ms")
stats = {}
stats['time'] = str(round(t1, 1))
stats['frames'] = str(args.number_iter * n)
stats['fps'] = str(args.number_iter * n / t1)
stats_file = result_dir + "/stats.json"
with open(stats_file, 'w') as f:
json.dump(stats, f)
del exec_net
del plugin
indicies_validation = [40, 63, 43, 55, 99, 101, 19, 46] #[40]
val_id = 1
infer_time = 0
process_time_start = time.time()
progress_file_path = os.path.join(png_directory, "i_progress.txt")
for idx in indicies_validation:
input_data_transposed=input_data[idx:(idx+batch_size)].transpose(0,3,1,2)
start_time = time.time()
res = exec_net.infer(inputs={input_blob:input_data_transposed[:,:n_channels]})
# Save the predictions to array
predictions = res[out_blob]
time_elapsed = time.time()-start_time
infer_time += time_elapsed
plotDiceScore(idx,input_data_transposed,label_data[[idx]].transpose(0,3,1,2),predictions,True, round(time_elapsed*1000))
progressUpdate(progress_file_path, time.time()-process_time_start, val_id, len(indicies_validation))
val_id += 1
total_time = round(time.time() - process_time_start, 2)
# Stats Json file for output mp4 file
stats = {}
stats['time'] = str(total_time)
stats['frames'] = str(val_id)
stats['fps'] = str(round(val_id / total_time,2))
with open(os.path.join(png_directory, 'stats.json'), 'w') as json_file:
json.dump(stats, json_file)
def main():
args = build_argparser().parse_args()
account_name = args.account_name
account_key = args.account_key
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
if account_name is "" or account_key is "":
print("Invalid account name or account key!")
sys.exit(1)
elif account_name is not None and account_key is None:
print("Please provide account key using -ak option!")
sys.exit(1)
elif account_name is None and account_key is not None:
print("Please provide account name using -an option!")
sys.exit(1)
elif account_name is None and account_key is None:
upload_azure = 0
else:
print("Uploading the results to Azure storage \"" + account_name +
"\"")
upload_azure = 1
create_cloud_container(account_name, account_key)
#if args.input == 'cam':
#input_stream = 0
#else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
cap = cv2.VideoCapture(input_stream)
if cap is None or not cap.isOpened():
print('Warning: unable to open video source: ', args.input)
sys.exit(1)
# Initialise the class
infer_network = Network()
# Load the network to IE plugin to get shape of input layer
n, c, h, w = infer_network.load_model(args.model, args.device, 1, 1, 0,
args.cpu_extension)
print("To stop the execution press Esc button")
initial_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
initial_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
video_len = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
store_aisle = cv2.VideoWriter(
os.path.join(args.output_dir, job_id, "store_aisle.mp4"),
cv2.VideoWriter_fourcc(*'avc1'), fps, (initial_w, initial_h), True)
progress_file_path = os.path.join(args.output_dir, job_id,
'i_progress.txt')
infer_time_start = time.time()
frame_count = 1
ret, frame = cap.read()
while cap.isOpened():
ret, next_frame = cap.read()
if not ret:
break
frame_count = frame_count + 1
in_frame = cv2.resize(next_frame, (w, h))
# Change data layout from HWC to CHW
in_frame = in_frame.transpose((2, 0, 1))
in_frame = in_frame.reshape((n, c, h, w))
# Start asynchronous inference for specified request.
inf_start = time.time()
infer_network.exec_net(0, in_frame)
# Wait for the result
infer_network.wait(0)
det_time = time.time() - inf_start
people_count = 0
# Results of the output layer of the network
res = infer_network.get_output(0)
for obj in res[0][0]:
# Draw only objects when probability more than specified threshold
if obj[2] > args.prob_threshold:
xmin = int(obj[3] * initial_w)
ymin = int(obj[4] * initial_h)
xmax = int(obj[5] * initial_w)
ymax = int(obj[6] * initial_h)
class_id = int(obj[1])
# Draw bounding box
color = (min(class_id * 12.5,
255), min(class_id * 7,
255), min(class_id * 5, 255))
cv2.rectangle(frame, (xmin, ymin), (xmax, ymax), color, 2)
people_count = people_count + 1
people_count_message = "People Count : " + str(people_count)
inf_time_message = "Inference time: {:.3f} ms".format(det_time * 1000)
cv2.putText(frame, inf_time_message, (15, 25),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
cv2.putText(frame, people_count_message, (15, 65),
cv2.FONT_HERSHEY_COMPLEX, 1, (255, 255, 255), 2)
store_aisle.write(frame)
time_interval = MULTIPLICATION_FACTOR * fps
if frame_count % time_interval == 0:
apply_time_stamp_and_save(frame, people_count, upload_azure)
if frame_count % 10 == 0:
progressUpdate(progress_file_path,
int(time.time() - infer_time_start), frame_count,
video_len)
frame = next_frame
if args.output_dir:
total_time = round(time.time() - infer_time_start, 2)
stats = {}
stats['time'] = str(total_time)
stats['frames'] = str(frame_count)
stats['fps'] = str(round(frame_count / total_time, 2))
with open(os.path.join(args.output_dir, job_id, 'stats.json'),
'w') as json_file:
json.dump(stats, json_file)
cap.release()
infer_network.clean()
def main():
"""
Load the network and parse the output.
:return: None
"""
global DELAY
global CLIENT
global SIG_CAUGHT
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
logger = log.getLogger()
render_time = 0
roi_x = args.pointx
roi_y = args.pointy
roi_w = args.width
roi_h = args.height
if args.input == 'cam':
input_stream = 0
else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
cap = cv2.VideoCapture(input_stream)
if not cap.isOpened():
logger.error("ERROR! Unable to open video source")
sys.exit(1)
if input_stream:
# Adjust DELAY to match the number of FPS of the video file
DELAY = 1000 / cap.get(cv2.CAP_PROP_FPS)
# Initialise the class
infer_network = Network()
# Load the network to IE plugin to get shape of input layer
n, c, h, w = infer_network.load_model(args.model, args.device, 1, 1, 0,
args.cpu_extension)
ret, frame = cap.read()
video_len = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_count = 0
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
result_file = open(
os.path.join(args.output_dir, str(job_id), 'output.txt'), "w")
progress_file_path = os.path.join(args.output_dir, str(job_id),
'i_progress.txt')
infer_time_start = time.time()
while ret:
dims = ""
ret, next_frame = cap.read()
if not ret:
break
initial_wh = [cap.get(3), cap.get(4)]
if next_frame is None:
log.error("ERROR! blank FRAME grabbed")
break
# If either default values or negative numbers are given,
# then we will default to start of the FRAME
if roi_x <= 0 or roi_y <= 0:
roi_x = 0
roi_y = 0
if roi_w <= 0:
roi_w = next_frame.shape[1]
if roi_h <= 0:
roi_h = next_frame.shape[0]
key_pressed = cv2.waitKey(int(DELAY))
selected_region = [roi_x, roi_y, roi_w, roi_h]
selected_region = [roi_x, roi_y, roi_w, roi_h]
x_max1 = str(selected_region[0])
x_min1 = str(selected_region[0] + selected_region[2])
y_min1 = str(selected_region[1] + selected_region[3])
y_max1 = str(selected_region[1])
in_frame_fd = cv2.resize(next_frame, (w, h))
# Change data layout from HWC to CHW
in_frame_fd = in_frame_fd.transpose((2, 0, 1))
in_frame_fd = in_frame_fd.reshape((n, c, h, w))
# Start asynchronous inference for specified request.
inf_start = time.time()
infer_network.exec_net(0, in_frame_fd)
# Wait for the result
infer_network.wait(0)
det_time = time.time() - inf_start
# Results of the output layer of the network
res = infer_network.get_output(0)
# Parse SSD output
ssd_out(res, args, initial_wh, selected_region)
est = str(render_time * 1000)
time1 = round(det_time * 1000)
Worker = INFO.safe
out_list = [
str(frame_count), x_min1, y_min1, x_max1, y_max1,
str(Worker), est,
str(time1)
]
for i in range(len(out_list)):
dims += out_list[i] + ' '
dims += '\n'
result_file.write(dims)
render_start = time.time()
render_end = time.time()
render_time = render_end - render_start
frame_count += 1
if frame_count % 10 == 0:
progressUpdate(progress_file_path,
int(time.time() - infer_time_start), frame_count,
video_len)
frame = next_frame
if key_pressed == 27:
print("Attempting to stop background threads")
break
progressUpdate(progress_file_path, int(time.time() - infer_time_start),
frame_count, video_len)
if args.output_dir is None:
cv2.destroyAllWindows()
else:
total_time = round(time.time() - infer_time_start, 2)
stats = {}
stats["time"] = str(total_time)
stats["frames"] = str(frame_count)
stats["fps"] = str(round(frame_count / total_time, 2))
with open(os.path.join(args.output_dir, str(job_id), 'stats.json'),
'w') as json_file:
json.dump(stats, json_file)
infer_network.clean()
cap.release()
cv2.destroyAllWindows()
def main():
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = build_argparser().parse_args()
model_xml = args.model
model_bin = os.path.splitext(model_xml)[0] + ".bin"
# Plugin initialization for specified device and load extensions library if specified
log.info("Initializing plugin for {} device...".format(args.device))
ie = IECore()
#plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
log.info("Loading plugins for {} device...".format(args.device))
ie.add_extension(args.cpu_extension, "CPU")
# log.info("Loading plugins for {} device...".format(args.device))
# plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if args.device == "CPU":
#supported_layers = plugin.get_supported_layers(net)
supported_layers = ie.query_network(net, "CPU")
not_supported_layers = [
l for l in net.layers.keys() if l not in supported_layers
]
if len(not_supported_layers) != 0:
log.error(
"Following layers are not supported by the plugin for specified device {}:\n {}"
.format(ie.device, ', '.join(not_supported_layers)))
log.error(
"Please try to specify cpu extensions library path in sample's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(
net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(
net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
log.info("Loading IR to the plugin...")
#exec_net = plugin.load(network=net, num_requests=2)
## Removed spaces ################
exec_net = ie.load_network(network=net,
num_requests=2,
device_name=args.device)
# Read and pre-process input image
if isinstance(net.inputs[input_blob], list):
n, c, h, w = net.inputs[input_blob]
else:
n, c, h, w = net.inputs[input_blob].shape
del net
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
out_file_name = os.path.splitext(os.path.basename(args.input))[0]
if args.output_dir:
out_path = os.path.join(args.output_dir, job_id,
out_file_name + '.mp4')
if args.labels:
with open(args.labels, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
cur_request_id = 0
next_request_id = 1
#job_id = os.environ['PBS_JOBID']
#job_id = "12345"
inf_progress_file_path = os.path.join(args.output_dir, job_id,
'i_progress.txt')
# Post Progress file update file
ren_progress_file_path = os.path.join(args.output_dir, job_id,
'post_progress.txt')
is_async_mode = True
fps_sum = 0
frame_count = 0
read_time = 0
result_list = []
try:
# Set up the capture stream
input_stream = args.input
cap = cv2.VideoCapture(input_stream)
if cap.isOpened():
video_len = args.count
log.info("Live stream running at {} fps".format(cv2.CAP_PROP_FPS))
initial_w = cap.get(3)
initial_h = cap.get(4)
cap.set(cv2.CAP_PROP_BUFFERSIZE, 3)
log.info("Starting inference in async mode...")
infer_time_start = time.time()
while frame_count < video_len:
ret, frame = cap.read()
if not ret:
break
in_frame = cv2.resize(frame, (w, h))
in_frame = in_frame.transpose(
(2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
# Main sync point:
# in the truly Async mode we start the NEXT infer request, while waiting for the CURRENT to complete
# in the regular mode we start the CURRENT request and immediately wait for it's completion
inf_start = time.time()
if is_async_mode:
exec_net.start_async(request_id=next_request_id,
inputs={input_blob: in_frame})
else:
exec_net.start_async(request_id=cur_request_id,
inputs={input_blob: in_frame})
read_time_0 = time.time()
if exec_net.requests[cur_request_id].wait(-1) == 0:
inf_end = time.time()
det_time = inf_end - inf_start
#Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
processBoxes(frame_count, res, labels_map,
args.prob_threshold, frame, result_list,
det_time)
read_time += time.time() - read_time_0
frame_count += 1
#Write data to progress tracker
if frame_count % 10 == 0:
progressUpdate(inf_progress_file_path,
time.time() - infer_time_start, frame_count,
video_len)
key = cv2.waitKey(1)
if key == 27:
break
if (9 == key):
is_async_mode = not is_async_mode
log.info("Switched to {} mode".format(
"async" if is_async_mode else "sync"))
if is_async_mode:
cur_request_id, next_request_id = next_request_id, cur_request_id
##End while loop /
cap.release()
log.info("{} seconds were spent reading".format(read_time))
if args.output_dir is None:
cv2.destroyAllWindows()
else:
total_time = time.time() - infer_time_start
stats = {}
stats["time"] = str(round(total_time, 2))
stats["fps"] = str(round(frame_count / total_time, 2)),
stats["frames"] = str(frame_count)
with open(os.path.join(args.output_dir, job_id, 'stats.json'),
'w') as f:
json.dump(stats, f)
o_video = os.path.join(args.output_dir, job_id, 'output.mp4')
post_process_t = time.time()
postProcess(result_list, int(initial_w), int(initial_h), labels_map,
o_video, is_async_mode, ren_progress_file_path)
log.info("Post processing time: {0} sec".format(time.time() -
post_process_t))
finally:
del exec_net
del ie
def main():
"""
Load the network and parse the output.
:return: None
"""
global INFO
global DELAY
global POSE_CHECKED
log.basicConfig(format="[ %(levelname)s ] %(message)s",
level=log.INFO,
stream=sys.stdout)
args = args_parser().parse_args()
logger = log.getLogger()
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
#if args.input == 'cam':
# input_stream = 0
#else:
input_stream = args.input
assert os.path.isfile(args.input), "Specified input file doesn't exist"
cap = cv2.VideoCapture(input_stream)
initial_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
initial_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
video_len = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
fps = int(cap.get(cv2.CAP_PROP_FPS))
shopper = cv2.VideoWriter(
os.path.join(args.output_dir, job_id, "shopper.mp4"),
cv2.VideoWriter_fourcc(*"AVC1"), fps, (initial_w, initial_h), True)
frame_count = 0
progress_file_path = os.path.join(args.output_dir, job_id,
'i_progress.txt')
infer_time_start = time.time()
if input_stream:
cap.open(args.input)
# Adjust DELAY to match the number of FPS of the video file
DELAY = 1000 / cap.get(cv2.CAP_PROP_FPS)
if not cap.isOpened():
logger.error("ERROR! Unable to open video source")
return
# Initialise the class
infer_network = Network()
infer_network_pose = Network()
# Load the network to IE plugin to get shape of input layer
plugin, (n_fd, c_fd, h_fd,
w_fd) = infer_network.load_model(args.model, args.device, 1, 1, 0,
args.cpu_extension)
n_hp, c_hp, h_hp, w_hp = infer_network_pose.load_model(
args.posemodel, args.device, 1, 3, 0, args.cpu_extension, plugin)[1]
ret, frame = cap.read()
while ret:
looking = 0
ret, next_frame = cap.read()
frame_count += 1
if not ret:
print("checkpoint *BREAKING")
break
if next_frame is None:
log.error("checkpoint ERROR! blank FRAME grabbed")
break
initial_wh = [cap.get(3), cap.get(4)]
in_frame_fd = cv2.resize(next_frame, (w_fd, h_fd))
# Change data layout from HWC to CHW
in_frame_fd = in_frame_fd.transpose((2, 0, 1))
in_frame_fd = in_frame_fd.reshape((n_fd, c_fd, h_fd, w_fd))
# Start asynchronous inference for specified request
inf_start_fd = time.time()
infer_network.exec_net(0, in_frame_fd)
# Wait for the result
infer_network.wait(0)
det_time_fd = time.time() - inf_start_fd
# Results of the output layer of the network
res = infer_network.get_output(0)
# Parse face detection output
faces = face_detection(res, args, initial_wh)
if len(faces) != 0:
# Look for poses
for res_hp in faces:
xmin, ymin, xmax, ymax = res_hp
head_pose = frame[ymin:ymax, xmin:xmax]
in_frame_hp = cv2.resize(head_pose, (w_hp, h_hp))
in_frame_hp = in_frame_hp.transpose((2, 0, 1))
in_frame_hp = in_frame_hp.reshape((n_hp, c_hp, h_hp, w_hp))
inf_start_hp = time.time()
infer_network_pose.exec_net(0, in_frame_hp)
infer_network_pose.wait(0)
det_time_hp = time.time() - inf_start_hp
# Parse head pose detection results
angle_p_fc = infer_network_pose.get_output(0, "angle_p_fc")
angle_y_fc = infer_network_pose.get_output(0, "angle_y_fc")
if ((angle_y_fc > -22.5) & (angle_y_fc < 22.5) &
(angle_p_fc > -22.5) & (angle_p_fc < 22.5)):
looking += 1
POSE_CHECKED = True
INFO = INFO._replace(looker=looking)
else:
INFO = INFO._replace(looker=looking)
else:
INFO = INFO._replace(looker=0)
# Draw performance stats
inf_time_message = "Face Inference time: {:.3f} ms.".format(
det_time_fd * 1000)
if POSE_CHECKED:
cv2.putText(
frame, "Head pose Inference time: {:.3f} ms.".format(
det_time_hp * 1000), (0, 35), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (255, 255, 255), 1)
cv2.putText(frame, inf_time_message, (0, 15), cv2.FONT_HERSHEY_COMPLEX,
0.5, (255, 255, 255), 1)
cv2.putText(frame, "Shopper: {}".format(INFO.shopper), (0, 90),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
cv2.putText(frame, "Looker: {}".format(INFO.looker), (0, 110),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
shopper.write(frame)
if (frame_count % 10 == 0) or ((frame_count + 1) / video_len == 1):
print("Frame Count: ", frame_count, "video length", video_len)
progressUpdate(progress_file_path,
int(time.time() - infer_time_start),
frame_count + 1, video_len)
frame = next_frame
if args.output_dir:
total_time = round(time.time() - infer_time_start, 2)
stats = {}
stats['time'] = str(total_time)
stats['frames'] = str(video_len)
stats['fps'] = str(round(video_len / total_time, 2))
with open(os.path.join(args.output_dir, job_id, 'stats.json'),
'w') as json_file:
json.dump(stats, json_file)
infer_network.clean()
infer_network_pose.clean()
cap.release()
def main():
# Plugin initialization for specified device and load extensions library
global rolling_log
#defaultTarget = TARGET_DEVICE
env_parser()
args_parser()
check_args()
parse_conf_file()
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
# if TARGET_DEVICE not in acceptedDevices:
# print ("Unsupporterd device " + TARGET_DEVICE + ". Defaulting to CPU")
# TARGET_DEVICE = 'CPU'
print("Initializing plugin for {} device...".format(TARGET_DEVICE))
#plugin = IEPlugin(device=TARGET_DEVICE)
ie = IECore()
if CPU_EXTENSION and 'CPU' == TARGET_DEVICE:
#plugin.add_cpu_extension(CPU_EXTENSION)
ie.add_extension(CPU_EXTENSION, "CPU")
# Read IR
print("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
assert len(net.inputs.keys()) == 1, "Sample supports only single input topologies"
assert len(net.outputs) == 1, "Sample supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
# Load the IR
print("Loading IR to the plugin...")
#exec_net = plugin.load(network=net, num_requests=2)
exec_net = ie.load_network(network=net, num_requests=2, device_name=TARGET_DEVICE)
# Read and pre-process input image
n, c, h, w = net.inputs[input_blob].shape
del net
minFPS = min([i.cap.get(cv2.CAP_PROP_FPS) for i in videoCaps])
minlength = min([i.cap.get(cv2.CAP_PROP_FRAME_COUNT) for i in videoCaps])
for vc in videoCaps:
vc.rate = int(math.ceil(vc.length/minlength))
print(minFPS)
waitTime = int(round(1000 / minFPS / len(videoCaps))) # wait time in ms between showing frames
frames_sum = 0
for vc in videoCaps:
vc.init_vw(h, w, minFPS)
frames_sum += vc.length
statsWidth = w if w > 345 else 345
statsHeight = h if h > (len(videoCaps) * 20 + 15) else (len(videoCaps) * 20 + 15)
statsVideo = cv2.VideoWriter(os.path.join(output_dir,'Statistics.mp4'), cv2.VideoWriter_fourcc(*"AVC1"), minFPS, (statsWidth, statsHeight), True)
if not statsVideo.isOpened():
print ("Couldn't open stats video for writing")
sys.exit(4)
# Read the labels file
if labels_file:
with open(labels_file, 'r') as f:
labels_map = [x.strip() for x in f]
else:
labels_map = None
# Init a rolling log to store events
rolling_log_size = int((h - 15) / 20)
rolling_log = collections.deque(maxlen=rolling_log_size)
# Init inference request IDs
cur_request_id = 0
next_request_id = 1
# Start with async mode enabled
is_async_mode = True
if not UI_OUTPUT:
# Arrange windows so they are not overlapping
#arrange_windows(w, h)
print("To stop the execution press Esc button")
no_more_data = False
for vc in videoCaps:
vc.start_time = datetime.datetime.now()
frame_count = 0
progress_file_path = os.path.join(output_dir, job_id, 'i_progress.txt')
infer_start_time = time.time()
#Start while loop
while True:
# If all video captures are closed stop the loop
if False not in [videoCap.closed for videoCap in videoCaps]:
print("I broke here line 387")
break
no_more_data = False
# loop over all video captures
for idx, videoCapInfer in enumerate(videoCaps):
# read the next frame
#print("Video {0} has length {1} and fps {2}".format(idx, videoCapInfer.length, videoCapInfer.fps))
if not videoCapInfer.closed:
#print("ID {0}".format(idx))
vfps = int(round(videoCapInfer.cap.get(cv2.CAP_PROP_FPS)))
#for i in range(0, int(round(vfps / minFPS))):
for i in range(videoCapInfer.rate):
frame_count += 1
#print("i = {0}".format(i))
ret, frame = videoCapInfer.cap.read()
videoCapInfer.cur_frame_count += 1
# If the read failed close the program
if not ret:
videoCapInfer.closed = True
no_more_data = True
break
if videoCapInfer.closed:
print("Video {0} is done".format(idx))
print("Video has {0} frames ".format(videoCapInfer.length))
break
# Copy the current frame for later use
videoCapInfer.cur_frame = frame.copy()
videoCapInfer.initial_w = videoCapInfer.cap.get(3)
videoCapInfer.initial_h = videoCapInfer.cap.get(4)
# Resize and change the data layout so it is compatible
in_frame = cv2.resize(videoCapInfer.cur_frame, (w, h))
in_frame = in_frame.transpose((2, 0, 1)) # Change data layout from HWC to CHW
in_frame = in_frame.reshape((n, c, h, w))
infer_start = datetime.datetime.now()
if is_async_mode:
exec_net.start_async(request_id=next_request_id, inputs={input_blob: in_frame})
# Async enabled and only one video capture
if(len(videoCaps) == 1):
videoCapResult = videoCapInfer
# Async enabled and more than one video capture
else:
# Get previous index
videoCapResult = videoCaps[idx - 1 if idx - 1 >= 0 else len(videoCaps) - 1]
else:
# Async disabled
exec_net.start_async(request_id=cur_request_id, inputs={input_blob: in_frame})
videoCapResult = videoCapInfer
if exec_net.requests[cur_request_id].wait(-1) == 0:
infer_end = datetime.datetime.now()
infer_duration = infer_end - infer_start
current_count = 0
# Parse detection results of the current request
res = exec_net.requests[cur_request_id].outputs[out_blob]
for obj in res[0][0]:
class_id = int(obj[1])
# Draw only objects when probability more than specified threshold
if (obj[2] > PROB_THRESHOLD and
videoCapResult.req_label in labels_map and
labels_map.index(videoCapResult.req_label) == class_id - 1):
current_count += 1
xmin = int(obj[3] * videoCapResult.initial_w)
ymin = int(obj[4] * videoCapResult.initial_h)
xmax = int(obj[5] * videoCapResult.initial_w)
ymax = int(obj[6] * videoCapResult.initial_h)
# Draw box
cv2.rectangle(videoCapResult.cur_frame, (xmin, ymin), (xmax, ymax), (0, 255, 0), 4, 16)
if videoCapResult.candidate_count is current_count:
videoCapResult.candidate_confidence += 1
else:
videoCapResult.candidate_confidence = 0
videoCapResult.candidate_count = current_count
if videoCapResult.candidate_confidence is FRAME_THRESHOLD:
videoCapResult.candidate_confidence = 0
if current_count > videoCapResult.last_correct_count:
videoCapResult.total_count += current_count - videoCapResult.last_correct_count
if current_count is not videoCapResult.last_correct_count:
if UI_OUTPUT:
currtime = datetime.datetime.now().strftime("%H:%M:%S")
fr = FrameInfo(videoCapResult.frames, current_count, currtime)
videoCapResult.countAtFrame.append(fr)
new_objects = current_count - videoCapResult.last_correct_count
for _ in range(new_objects):
string = "{} - {} detected on {}".format(time.strftime("%H:%M:%S"), videoCapResult.req_label, videoCapResult.cap_name)
rolling_log.append(string)
videoCapResult.frames+=1
videoCapResult.last_correct_count = current_count
else:
videoCapResult.frames+=1
videoCapResult.cur_frame = cv2.resize(videoCapResult.cur_frame, (w, h))
if not UI_OUTPUT:
# Add log text to each frame
log_message = "Async mode is on." if is_async_mode else \
"Async mode is off."
cv2.putText(videoCapResult.cur_frame, log_message, (15, 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
log_message = "Total {} count: {}".format(videoCapResult.req_label, videoCapResult.total_count)
cv2.putText(videoCapResult.cur_frame, log_message, (10, h - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
log_message = "Current {} count: {}".format(videoCapResult.req_label, videoCapResult.last_correct_count)
cv2.putText(videoCapResult.cur_frame, log_message, (10, h - 30), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
cv2.putText(videoCapResult.cur_frame, 'Infer wait: %0.3fs' % (infer_duration.total_seconds()), (10, h - 70), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
# Display inferred frame and stats
stats = numpy.zeros((statsHeight, statsWidth, 1), dtype = 'uint8')
for i, log in enumerate(rolling_log):
cv2.putText(stats, log, (10, i * 20 + 15), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
#cv2.imshow(STATS_WINDOW_NAME, stats)
if idx == 0:
stats = cv2.cvtColor(stats, cv2.COLOR_GRAY2BGR)
#Write
statsVideo.write(stats)
end_time = datetime.datetime.now()
cv2.putText(videoCapResult.cur_frame, 'FPS: %0.2fs' % (1 / (end_time - videoCapResult.start_time).total_seconds()), (10, h - 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
#cv2.imshow(videoCapResult.cap_name, videoCapResult.cur_frame)
videoCapResult.start_time = datetime.datetime.now()
#Write
videoCapResult.video.write(videoCapResult.cur_frame)
if frame_count%10 == 0:
progressUpdate(progress_file_path, time.time()-infer_start_time, frame_count, frames_sum)
# Wait if necessary for the required time
#key = cv2.waitKey(waitTime)
key = cv2.waitKey(1)
# Esc key pressed
if key == 27:
cv2.destroyAllWindows()
del exec_net
#del plugin
del ie
print("Finished")
return
# Tab key pressed
if key == 9:
is_async_mode = not is_async_mode
print("Switched to {} mode".format("async" if is_async_mode else "sync"))
if is_async_mode:
# Swap infer request IDs
cur_request_id, next_request_id = next_request_id, cur_request_id
# Loop video if LOOP_VIDEO = True and input isn't live from USB camera
if LOOP_VIDEO and not videoCapInfer.is_cam:
vfps = int(round(videoCapInfer.cap.get(cv2.CAP_PROP_FPS)))
# If a video capture has ended restart it
if (videoCapInfer.cur_frame_count > videoCapInfer.cap.get(cv2.CAP_PROP_FRAME_COUNT) - int(round(vfps / minFPS))):
videoCapInfer.cur_frame_count = 0
videoCapInfer.cap.set(cv2.CAP_PROP_POS_FRAMES, 0)
if no_more_data:
progressUpdate(progress_file_path, time.time()-infer_start_time, frames_sum, frames_sum)
break
#End of while loop--------------------
no_more_data = True
t2 = round(time.time()-infer_start_time,2)
for videos in videoCaps:
print(videos.length)
print(videos.closed)
print("End loop")
print("Total time {0}".format(t2))
print("Total frame count {0}".format(frame_count))
print("fps {0}".format(frame_count/t2))
stats = {}
stats['time'] = str(t2) # Total Time
stats['frames'] = str(frame_count)
stats['fps'] = str(round(frame_count / t2,2))
with open(os.path.join(output_dir, job_id, 'stats.json'), 'w') as json_file:
json.dump(stats, json_file)
for vc in videoCaps:
print("Frames processed {}".format(vc.cur_frame_count))
print("Frames count {}".format(vc.length))
for vc in videoCaps:
vc.video.release()
vc.cap.release()
if no_more_data:
break
def intruder_detector():
"""
Process the input source frame by frame and detects intruder, if any.
:return status: 0 on success, negative value on failure
"""
global CONF_CANDIDATE_CONFIDENCE
global LOG_WIN_HEIGHT
global LOG_WIN_WIDTH
global CONF_FILE
global video_caps
global conf_labels_file_path
parse_args()
if not os.path.isfile(CONF_FILE):
return -12, ""
if not os.path.isfile(conf_labels_file_path):
return -13, ""
# Creates subdirectory to save output snapshots
pathlib.Path(os.getcwd() + '/output/').mkdir(parents=True, exist_ok=True)
# Read the configuration file
ret, req_labels = get_input()
if ret != 0:
return ret, req_labels[0]
if not video_caps:
return -14, ''
# Get the labels that are used in the application
ret, label_names, used_labels = get_used_labels(req_labels)
if ret != 0:
return ret, ''
if True not in used_labels:
return -15, ''
# Init a rolling log to store events
rolling_log_size = int((LOG_WIN_HEIGHT - 15) / 20)
log_list = collections.deque(maxlen=rolling_log_size)
# Open a file for intruder logs
log_file = open(LOG_FILE_PATH, 'w')
if not log_file:
return -16, ''
# Initializing VideoWriter for each source
for video_cap in video_caps:
ret, ret_value = video_cap.init_vw(int(video_cap.input_height),
int(video_cap.input_width))
if ret != 0:
return ret, ret_value
# Initialise the class
infer_network = Network()
# Load the network to IE plugin to get shape of input layer
n, c, h, w = infer_network.load_model(model_xml, TARGET_DEVICE, 1, 1, 0,
CPU_EXTENSION)
min_fps = min([i.vc.get(cv2.CAP_PROP_FPS) for i in video_caps])
no_more_data = [False] * len(video_caps)
start_time = time.time()
inf_time = 0
fourcc = cv2.VideoWriter_fourcc(*'avc1')
job_id = os.environ['PBS_JOBID']
job_id = job_id.rstrip().split('.')[0]
statsVideo = cv2.VideoWriter(
os.path.join(output_dir, str(job_id), 'Statistics.mp4'), fourcc,
min_fps, (LOG_WIN_WIDTH, LOG_WIN_HEIGHT), True)
progress_file_path = os.path.join(output_dir, str(job_id),
'i_progress.txt')
infer_start_time = time.time()
# Main loop starts here. Loop over all the video captures
while True:
for idx, video_cap in enumerate(video_caps):
# Get a new frame
vfps = int(round(video_cap.vc.get(cv2.CAP_PROP_FPS)))
for i in range(0, int(round(vfps / min_fps))):
ret, video_cap.frame = video_cap.vc.read()
video_cap.loop_frames += 1
# If no new frame or error in reading a frame, exit the loop
if not ret:
no_more_data[idx] = True
break
if no_more_data[idx]:
stream_end_frame = numpy.zeros((int(
video_cap.input_height), int(video_cap.input_width), 1),
dtype='uint8')
stream_end_message = "Stream from {} has ended.".format(
video_cap.cam_name)
cv2.putText(stream_end_frame, stream_end_message,
(int(video_cap.input_width / 2) - 30,
int(video_cap.input_height / 2) - 30),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 255), 1)
continue
for i in range(video_cap.no_of_labels):
video_cap.current_count[i] = 0
video_cap.changed_count[i] = False
# Resize to expected size (in model .xml file)
# Input frame is resized to infer resolution
in_frame = cv2.resize(video_cap.frame, (w, h))
# PRE-PROCESS STAGE:
# Convert image to format expected by inference engine
# IE expects planar, convert from packed
# Change data layout from HWC to CHW
in_frame = in_frame.transpose((2, 0, 1))
in_frame = in_frame.reshape((n, c, h, w))
# Start asynchronous inference for specified request.
inf_start = time.time()
infer_network.exec_net(0, in_frame)
# Wait for the result
if infer_network.wait(0) == 0:
inf_time = time.time() - inf_start
# Results of the output layer of the network
res = infer_network.get_output(0)
for obj in res[0][0]:
label = int(obj[1]) - 1
# Draw the bounding box around the object when the probability is more than specified threshold
if obj[2] > CONF_THRESHOLD_VALUE and used_labels[label]:
video_cap.current_count[label] += 1
xmin = int(obj[3] * video_cap.input_width)
ymin = int(obj[4] * video_cap.input_height)
xmax = int(obj[5] * video_cap.input_width)
ymax = int(obj[6] * video_cap.input_height)
# Draw bounding box around the intruder detected
cv2.rectangle(video_cap.frame, (xmin, ymin),
(xmax, ymax), (0, 255, 0), 4, 16)
for i in range(video_cap.no_of_labels):
if video_cap.candidate_count[i] == video_cap.current_count[
i]:
video_cap.candidate_confidence[i] += 1
else:
video_cap.candidate_confidence[i] = 0
video_cap.candidate_count[i] = video_cap.current_count[
i]
if video_cap.candidate_confidence[
i] == CONF_CANDIDATE_CONFIDENCE:
video_cap.candidate_confidence[i] = 0
video_cap.changed_count[i] = True
else:
continue
if video_cap.current_count[
i] > video_cap.last_correct_count[i]:
video_cap.total_count[i] += video_cap.current_count[
i] - video_cap.last_correct_count[i]
det_objs = video_cap.current_count[
i] - video_cap.last_correct_count[i]
total_count = sum(video_cap.total_count)
for det_obj in range(det_objs):
current_time = time.strftime("%H:%M:%S")
log = "{} - Intruder {} detected on {}".format(
current_time, label_names[i],
video_cap.cam_name)
print(log)
log_list.append(log)
log_file.write(log + "\n")
event = Event(event_time=current_time,
intruder=label_names[i],
count=total_count,
frame=video_cap.frame_count)
video_cap.events.append(event)
snapshot_name = "output/intruder_{}.png".format(
total_count)
cv2.imwrite(snapshot_name, video_cap.frame)
video_cap.last_correct_count[i] = video_cap.current_count[
i]
# Create intruder log window, add logs to the frame and display it
log_window = numpy.zeros((LOG_WIN_HEIGHT, LOG_WIN_WIDTH, 1),
dtype='uint8')
for i, log in enumerate(log_list):
cv2.putText(log_window, log, (10, 20 * i + 15),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 255, 255), 1)
log_window = cv2.cvtColor(log_window, cv2.COLOR_GRAY2BGR)
statsVideo.write(log_window)
video_cap.frame_count += 1
# Video output
inf_time_message = "Inference time: {:.3f} ms".format(inf_time *
1000)
cv2.putText(video_cap.frame, inf_time_message,
(10, int(video_cap.input_height) - 30),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
fps_time = time.time() - start_time
fps_message = "FPS: {:.3f} fps".format(1 / fps_time)
cv2.putText(video_cap.frame, fps_message,
(10, int(video_cap.input_height) - 10),
cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
# Display the video output
video_cap.vw.write(video_cap.frame)
if video_cap.frame_count % 10 == 0:
progressUpdate(progress_file_path,
time.time() - infer_start_time,
video_cap.frame_count,
int(video_cap.vc.get(cv2.CAP_PROP_FRAME_COUNT)))
start_time = time.time()
# Loop video to mimic continuous input if LOOP_VIDEO flag is True
if LOOP_VIDEO and not video_cap.is_cam:
vfps = int(round(video_cap.vc.get(cv2.CAP_PROP_FPS)))
# If a video capture has ended restart it
if video_cap.loop_frames > video_cap.vc.get(
cv2.CAP_PROP_FRAME_COUNT) - int(round(vfps / min_fps)):
video_cap.loop_frames = 0
video_cap.vc.set(cv2.CAP_PROP_POS_FRAMES, 0)
if False not in no_more_data:
progressUpdate(progress_file_path,
time.time() - infer_start_time,
int(video_cap.vc.get(cv2.CAP_PROP_FRAME_COUNT)),
int(video_cap.vc.get(cv2.CAP_PROP_FRAME_COUNT)))
break
no_more_data = False
t2 = time.time() - infer_start_time
for videos in video_caps:
stats = {}
stats['time'] = str(round(t2, 2))
stats['fps'] = str(round(videos.frame_count / t2, 2))
stats['frames'] = str(videos.frame_count)
with open(os.path.join(output_dir, str(job_id), 'stats.json'), 'w') as f:
json.dump(stats, f)
infer_network.clean()
log_file.close()
return 0, ''