def visualize(image_list,
results,
labels,
output_dir='output/',
threshold=0.5):
# visualize the predict result
start_idx = 0
for idx, image_file in enumerate(image_list):
im_bboxes_num = results['boxes_num'][idx]
im_results = {}
if 'boxes' in results:
im_results['boxes'] = results['boxes'][start_idx:start_idx +
im_bboxes_num, :]
if 'label' in results:
im_results['label'] = results['label'][start_idx:start_idx +
im_bboxes_num]
if 'score' in results:
im_results['score'] = results['score'][start_idx:start_idx +
im_bboxes_num]
start_idx += im_bboxes_num
im = visualize_box_mask(image_file,
im_results,
labels,
threshold=threshold)
img_name = os.path.split(image_file)[-1]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
out_path = os.path.join(output_dir, img_name)
im.save(out_path, quality=95)
print("save result to: " + out_path)
def predict_video(model_dir, video_file="0", threshold=0.2, use_gpu=True, run_mode='fluid', output_dir='output/'):
if video_file == "0":
video_file = 0
detector = Detector(model_dir, use_gpu=use_gpu, run_mode=run_mode)
capture = cv2.VideoCapture(video_file)
fps = 25
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video_name = 'camera.mp4'
if video_file != 0:
video_name = os.path.split(video_file)[-1]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
out_path = os.path.join(output_dir, video_name)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
index = 1
while 1:
ret, frame = capture.read()
if not ret:
break
print('detect frame:%d' % index)
index += 1
results = detector.predict(frame, threshold)
im = visualize_box_mask(
frame,
results,
detector.config.labels,
mask_resolution=detector.config.mask_resolution)
im = np.array(im)
cv2.imshow("dsf", im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
writer.write(im)
writer.release()
def predict_video():
detector = Detector(FLAGS.model_dir,
use_gpu=FLAGS.use_gpu,
run_mode=FLAGS.run_mode)
capture = cv2.VideoCapture(FLAGS.video_file)
fps = 30
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
video_name = os.path.split(FLAGS.video_file)[-1]
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
out_path = os.path.join(FLAGS.output_dir, video_name)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
index = 1
while (1):
ret, frame = capture.read()
if not ret:
break
print('detect frame:%d' % (index))
index += 1
results = detector.predict(frame, FLAGS.threshold)
im = visualize_box_mask(
frame,
results,
detector.config.labels,
mask_resolution=detector.config.mask_resolution)
im = np.array(im)
writer.write(im)
writer.release()
def predict_video(detector, camera_id):
if camera_id != -1:
capture = cv2.VideoCapture(camera_id)
video_name = 'output.mp4'
else:
capture = cv2.VideoCapture(FLAGS.video_file)
video_name = os.path.split(FLAGS.video_file)[-1]
fps = 30
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
if not os.path.exists(FLAGS.output_dir):
os.makedirs(FLAGS.output_dir)
out_path = os.path.join(FLAGS.output_dir, video_name)
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
index = 1
while (1):
ret, frame = capture.read()
if not ret:
break
print('detect frame:%d' % (index))
index += 1
results = detector.predict(frame, FLAGS.threshold)
im = visualize_box_mask(
frame,
results,
detector.config.labels,
mask_resolution=detector.config.mask_resolution)
im = np.array(im)
writer.write(im)
if camera_id != -1:
cv2.imshow('Mask Detection', im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
writer.release()
def visualize(image_file,
results,
labels,
output_dir='output/',
threshold=0.5):
# visualize the predict result
im = visualize_box_mask(image_file, results, labels, threshold=threshold)
img_name = os.path.split(image_file)[-1]
if not os.path.exists(output_dir):
os.makedirs(output_dir)
out_path = os.path.join(output_dir, img_name)
im.save(out_path, quality=95)
print("save result to: " + out_path)
def pi_visualize(image_file,
results,
labels,
mask_resolution=14,
video_writer=None):
# visualize the predict result
im = visualize_box_mask(
image_file, results, labels, mask_resolution=mask_resolution)
# img_name = os.path.split(image_file)[-1]
# if not os.path.exists(output_dir):
# os.makedirs(output_dir)
# out_path = os.path.join(output_dir, img_name)
# img = cv2.cvtColor(, cv2.COLOR_RGB2BGR)
img = numpy.asarray(im)
cv2.imshow("mask", img)
if video_writer is not None:
video_writer.write(img)
c = cv2.waitKey(1)
def predict_video(self, video_file, camera_id):
video_out_name = 'output.mp4'
if camera_id != -1:
capture = cv2.VideoCapture(camera_id)
else:
capture = cv2.VideoCapture(video_file)
video_out_name = os.path.split(video_file)[-1]
# Get Video info : resolution, fps, frame count
width = int(capture.get(cv2.CAP_PROP_FRAME_WIDTH))
height = int(capture.get(cv2.CAP_PROP_FRAME_HEIGHT))
fps = int(capture.get(cv2.CAP_PROP_FPS))
frame_count = int(capture.get(cv2.CAP_PROP_FRAME_COUNT))
print("fps: %d, frame_count: %d" % (fps, frame_count))
if not os.path.exists(self.output_dir):
os.makedirs(self.output_dir)
out_path = os.path.join(self.output_dir, video_out_name)
fourcc = cv2.VideoWriter_fourcc(*'mp4v')
writer = cv2.VideoWriter(out_path, fourcc, fps, (width, height))
index = 1
while (1):
ret, frame = capture.read()
if not ret:
break
print('detect frame: %d' % (index))
index += 1
results = self.predict_image([frame], visual=False)
im = visualize_box_mask(frame,
results,
self.pred_config.labels,
threshold=self.threshold)
im = np.array(im)
writer.write(im)
if camera_id != -1:
cv2.imshow('Mask Detection', im)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
writer.release()
def predict_publish():
"""
This Function handles the video analytics pipeline that captures
the image in a video feed, apply the onject detection, analyze the
interest of event and notify visually. This function also sends the
metadata and processed frame to the Redis. It runs forever.
Args:
Nothing
Returns:
Nothing
"""
index = 1
detector = paddle_detect.Detector(FLAGS.model_dir,
use_gpu=FLAGS.use_gpu,
run_mode=FLAGS.run_mode)
event_type = int(prd.get("event_type"))
app_mode = int(prd.get("app_mode"))
event_manager = event_alert.EventManager(FLAGS.app_config_dir, event_type)
eventName = event_manager.getEventName(event_type)
event_type_prev = event_type
app_mode_prev = app_mode
capture = cv2.VideoCapture(FLAGS.video_feed, cv2.CAP_FFMPEG)
while True:
success, image = capture.read()
print('Reading')
if not success:
break
print('Reading Successful')
while True:
app_mode = int(prd.get("app_mode"))
event_type = int(prd.get("event_type"))
if event_type != event_type_prev or app_mode != app_mode_prev:
event_manager.updateROISettings(event_type)
eventName = event_manager.getEventName(event_type)
retval, buffer = cv2.imencode('.jpg', image)
pic_str = base64.b64encode(buffer)
pic_str = pic_str.decode()
prd.set(eventName, pic_str)
event_type_prev = event_type
app_mode_prev = app_mode
if app_mode != 1:
break
print('detect frame:%d' % (index))
index += 1
results = detector.predict(image, FLAGS.threshold)
#print(results['boxes'])
im = visualize_box_mask(
image,
results,
detector.config.labels,
mask_resolution=detector.config.mask_resolution)
im = np.array(im)
img = im
if event_type == 2:
img, result, appMetadata = event_manager.monitorVehicle(
results['boxes'], im)
elif event_type == 1:
img, result, appMetadata = event_manager.alertStrangerIntrusion(
results['boxes'], im)
elif event_type == 0:
img, result, appMetadata = event_manager.findEventStat(
results['boxes'], im, vehicleThres=5)
else:
img, result, appMetadata = event_manager.findEventStat(
results['boxes'], im, vehicleThres=5)
retval, buffer = cv2.imencode('.jpg', img)
pic_str = base64.b64encode(buffer)
pic_str = pic_str.decode()
appMetadatastr = json.dumps(appMetadata)
msg = {'appMetadata': appMetadatastr, 'image': pic_str}
_id = prd.xadd('camera:0', msg, maxlen=1000)
event_type_prev = event_type
if FLAGS.local_debug == True:
cv2.imshow("Evento", img)
cv2.waitKey(10)