def run_video():
PATH_VIDEO = 'D:/Binus/data/videos/VID_20190915_123023.mp4'
PATH_OUT_VIDEO = 'D:/Binus/data/videos/VID_20190915_123023_2.avi'
fo = FaceOrchestrator()
fs = FileVideoStream(PATH_VIDEO).start()
time.sleep(1.0)
frame = fs.read()
h, w, __ = frame.shape
out = cv2.VideoWriter(PATH_OUT_VIDEO,
cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'), 24,
(w, h))
i = 0
while fs.more():
i = i + 1
print('Processing frame ' + str(i))
try:
frame = fs.read()
fo.set_image_to_be_processed(frame)
# detect faces
fo.fd_method = 'AI'
fo.fd_ai_min_score = 0.6
fo.detect_faces()
fo.align_faces(target_face_percent=0.28)
# reconize faces
# fo.fr_max_delta = 0.6
# fo.fr_method = 'DELTA'
fo.fr_min_probability = 0.5
fo.fr_method = 'ML'
frame_with_label, names = fo.recognize_faces()
out.write(frame_with_label)
except:
pass
fs.stop()
out.release()
print('Finish.')
def run_detection(fast_mtcnn, filenames):
frames = []
frames_processed = 0
faces_detected = 0
batch_size = 60
start = time.time()
for filename in tqdm(filenames):
v_cap = FileVideoStream(filename).start()
v_len = int(v_cap.stream.get(cv2.CAP_PROP_FRAME_COUNT))
for j in range(v_len):
frame = v_cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
if len(frames) >= batch_size or j == v_len - 1:
faces = fast_mtcnn(frames)
for i in range(len(faces)):
file_name = str(j) + str(i) + '.jpg'
# cv2.imwrite(file_name,faces[i])
#print(faces)
frames_processed += len(frames)
faces_detected += len(faces)
frames = []
print(
f'Frames per second: {frames_processed / (time.time() - start):.3f},',
f'faces detected: {faces_detected}\r',
end='')
v_cap.stop()
class VideoStreamer(Camera):
def __init__(self, src=0, use_pi=-1, resolution=480, framerate=30):
super(VideoStreamer, self).__init__(use_pi=use_pi,
resolution=resolution,
framerate=framerate)
# VideoStream class is used for live stream.
# FileVideoStream class is used for streaming from a saved video.
if isinstance(src, int):
self.vs = VideoStream(src=src,
usePiCamera=self.use_pi > 0,
resolution=self.resolution,
framerate=self.framerate).start()
else:
self.vs = FileVideoStream(path=src)
# Let camera warm up
time.sleep(1.0)
# Returns the frame as a np array
def read(self):
if isinstance(self.vs, FileVideoStream):
return self.vs.stream.read()[1]
else:
return self.vs.read()
# Terminate the capture thread.
def stop(self):
self.vs.stop()
def livevideocorrection(filepath, modelname):
fvs = FileVideoStream(filepath, queue_size=128).start()
desired_frames = 250
frame_number = 0
last_time = time.monotonic()
while fvs.more() and frame_number <= desired_frames:
frame = fvs.read()
if frame_number % 10 == 0:
current_time = time.monotonic()
timedif = current_time - last_time
FPS = 10 / timedif
print('FPS:' + str(FPS))
last_time = time.monotonic()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
prediction = (predict_one(frame,
modelname).squeeze().detach().float().cpu())
prediction = prediction.permute(1, 2, 0).numpy()
prediction = cv2.cvtColor(prediction, cv2.COLOR_BGR2RGB)
cv2.putText(prediction, "Queue Size: {}".format(fvs.Q.qsize()),
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.putText(prediction, ('FPS: ' + str(FPS)), (750, 50),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.imshow('preview', prediction)
frame_number += 1
cv2.waitKey(1)
cv2.destroyAllWindows()
fvs.stop()
def main():
# Construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", required=True,
help="path to model file")
ap.add_argument("-c", "--confidence", type=float, default=0.95,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# Initialize our centroid tracker and frame dimensions
ct = CentroidTracker()
# Load our serialized model from disk
print("[INFO] Loading model...")
model = get_model(args["model"])
vs = FileVideoStream("/media/ave/ae722e82-1476-4374-acd8-a8a18ef8ae7a/Rana_vids/Nova_1/121/03-2018.05.25_06.24.23-17.mpg").start()
time.sleep(2.0)
# Loop over the frames from the video stream
while vs.more():
# Read the next frame from the video stream
frame = vs.read()
analyze_frame(frame, ct, model, args["confidence"])
# Cleanup
cv2.destroyAllWindows()
vs.stop()
def read_frames(self):
# start the file video stream thread and allow the buffer to
# start to fill
print("[INFO] starting video file thread...")
args = self.argument_parser()
output_path = self.cwd / Path(args.output_dir)
output_path.mkdir(parents=True, exist_ok=True)
fvs = FileVideoStream(args.video).start()
time.sleep(1.0)
# start the FPS timer
fps = FPS().start()
detector = mtcnn.MTCNN()
fno = 0
frames = VideoMeta(args.video).fps()
#print(meta)
# loop over frames from the video file stream
while fvs.more():
fno += 1
print(fno)
# grab the frame from the threaded video file stream, resize
# it, and convert it to grayscale (while still retaining 3
# channels)
frame = fvs.read()
if (fno % frames != 0):
continue
try:
frame = imutils.resize(frame, width=450)
except:
break
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# frame = np.dstack([frame, frame, frame])
if args.detect == "haarcascade":
results = self.face_cascade.detectMultiScale(frame, 1.3, 5)
if len(results) != 0:
self.crop(frame, results[0],
str(output_path / Path(str(fno) + ".jpg")))
else:
results = detector.detect_faces(frame)
# display the size of the queue on the frame
# cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()),
# (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
# show the frame and update the FPS counter
cv2.imshow("Frame", frame)
cv2.waitKey(1)
fps.update()
# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
fvs.stop()
def scan_file(self, fname):
vs = FileVideoStream(fname).start()
while True:
frame = vs.read()
# frame = imutils.resize(frame)
barcodes = pyzbar.decode(frame)
for code in barcodes:
(x, y, w, h) = code.rect
cv2.rectangle(frame, (x, y), (x + w, x + h), (0, 0, 255), 2)
bar_data = code.data.decode('utf-8')
bar_type = code.type
text = f'{bar_data} ({bar_type})'
cv2.putText(frame, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,
0.5, (0, 0, 255), 2)
cv2.imshow("Barcode Scanner", frame)
key = cv2.waitKey(1) & 0xFF
if key == ord('q'):
break
cv2.destroyAllWindows()
vs.stop()
def main(video_path):
video_path = 'sample_video/car_stopping_shorter_version.mp4'
fvs = FileVideoStream(video_path).start()
time.sleep(1.0)
fps = FPS().start()
# loop over frames from the video file stream
input_frames_list = []
number = 0
while fvs.more():
number += 1
frame = fvs.read()
if (frame is None):
continue
cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
ann_frame = stream_frames_to_model(frame, number)
cv2.imshow('frame', ann_frame)
cv2.waitKey(1)
fps.update()
# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
fvs.stop()
def livevideocorrection(filepath, modelname):
fvs = FileVideoStream(filepath, queue_size=128).start()
# ivs = InferenceDataStream(fvs, queue_size=25).start() # --> inference video stream reads from file video stream and forms queue of prepped images for inference
desired_frames = 250
frame_number = 0
last_time = time.monotonic()
while fvs.more() and frame_number <= desired_frames:
frame = fvs.read() # --> frame = ivs.read()
if frame_number % 10 == 0:
current_time = time.monotonic()
timedif = current_time - last_time
FPS = 10 / timedif
print('FPS:' + str(FPS))
last_time = time.monotonic()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
prediction = (
predict_one(frame, frame_number).squeeze().detach().cpu()
) # --> prediction = predict_one(frame, frame_number) #why? because logic for preparing images is now done asyncronously in ivs.
prediction = prediction.permute(1, 2, 0).numpy()
prediction = cv2.cvtColor(prediction, cv2.COLOR_BGR2RGB)
cv2.putText(prediction, "Queue Size: {}".format(fvs.Q.qsize()),
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.putText(prediction, ('FPS: ' + str(FPS)), (750, 50),
cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2)
cv2.imshow('preview', prediction)
frame_number += 1
cv2.waitKey(1)
cv2.destroyAllWindows()
fvs.stop()
def extract(input_path, output_folder, rate):
STR_FRAME_PATH = "{}\\frame{}.jpg"
video_args = {}
video_args["video"] = input_path
fvs = FileVideoStream(video_args["video"], queue_size=300).start()
time.sleep(1.0)
framecount = 0
fps = FPS().start()
while fvs.running():
frame = fvs.read()
img_raw = None
framecount = framecount + 1
if framecount % int(rate) == 0:
print("Extrating frame {}".format(framecount), end="\r")
cv2.imwrite(STR_FRAME_PATH.format(output_folder, str(framecount)),
frame)
else:
continue
fps.update()
fps.stop()
fvs.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
def read_feed():
#cap = VideoStream(usePiCamera=True, resolution=(VIDEO_WIDTH, VIDEO_HEIGHT))
cap = FileVideoStream('sample/fish3.mp4').start()
time.sleep(2.0)
frame = cap.read()
global VIDEO_HEIGHT, VIDEO_WIDTH
VIDEO_HEIGHT, VIDEO_WIDTH = frame.shape[:2]
try:
while True:
frame = cap.read()
jpeg_frame = cv2.imencode('.jpg', frame,
[int(cv2.IMWRITE_JPEG_QUALITY), 80])[1]
if VIDEO_IS_RECORDING:
VIDEO_WRITER_QUEUE.put(jpeg_frame)
#time.sleep(0.1)
yield (b'--frame\r\n'
b'Content-Type: image/jpeg\r\n\r\n' +
jpeg_frame.tostring() + b'\r\n')
finally:
cap.stop()
def compute_attributes(self):
input_stream = cv.VideoCapture(self.file_name)
self.sample_rate = input_stream.get(cv.CAP_PROP_FPS)
input_stream.release()
# Use multi-threading by Adrian of https://pyimagesearch.com
input_stream = FileVideoStream(self.file_name).start()
directograms = []
directogram_times = []
frame = input_stream.read()
last_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
self.shape = frame.shape
while input_stream.more():
if len(directograms) % 250 == 0:
print('Video Processing: {:.2f}s'.format(
len(directograms) / self.sample_rate))
frame = input_stream.read()
if not input_stream.more():
break
next_frame = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
# These parameters were taken from a demo in the documentation
flow = cv.calcOpticalFlowFarneback(last_frame, next_frame, None,
0.5, 3, 15, 3, 5, 1.2, 0)
directograms.append(get_directogram(flow))
directogram_times.append(len(directograms) / self.sample_rate)
last_frame = next_frame
self.envelope_times = np.array(directogram_times)
self.envelopes = get_impact_envelopes(directograms,
self.envelope_times)
self.envelope_sample_rate = self.sample_rate
input_stream.stop()
def main():
vid = (Path(__file__).parent / 'test.mp4').absolute()
stream = FileVideoStream(str(vid), queue_size=100).start()
time.sleep(1)
while stream.more():
img_data = stream.read()
if img_data is None:
break
img = Image(img_data)
transformed = Transformer(img)
image = transformed.cannify()
cropped_img = region(image)
lines = cv2.HoughLinesP(cropped_img.raw,
2,
np.pi / 180,
100,
np.array([]),
minLineLength=40,
maxLineGap=5)
avg_lines = utils.average_slope_intercept(img, lines)
lines_image = img.display_lines(avg_lines)
combined = img.combine(lines_image)
cv2.imshow('', combined.raw)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cv2.destroyAllWindows()
stream.stop()
def face_verification(self, path):
"""
Face verification from input file video Stream
:param path: Location of the video
:return name: Recognized person's name
"""
video_path = path
result = True
while (result):
vs = FileVideoStream(video_path).start()
frame = vs.read()
frame = ndimage.rotate(frame, 180)
frame = ndimage.rotate(frame, 90)
cap_image = frame
check = SPOOF_DETECTION().spoof_detection(video_path)
result = False
vs.stop()
cv2.destroyAllWindows()
if check == True:
name = obj.face_recognize(cap_image)
print('Recognized image {}'.format(name))
else:
print('SPOOFING ')
class ImageRouter(QtCore.QObject):
source_is_file = False
on_frame = QtCore.pyqtSignal(np.ndarray)
def __init__(self, filename=''):
super().__init__()
if filename == '':
self.vs = VideoStream(src=0).start()
self.source_is_file = False
else:
self.vs = FileVideoStream(filename).start()
self.source_is_file = True
def __del__(self):
self.vs.stop()
def tick(self):
if self.source_is_file and not self.vs.more():
return False
frame = self.vs.read()
if frame is None:
return False
self.on_frame.emit(frame)
return True
def run(self):
print("[INFO] Started ImageRouter")
while self.tick():
None
print("[INFO] Stopped ImageRouter")
def main(input_video_path, output_video_path, batch_size, async_workers_count):
frame_id = 0
video_path = 'sample_video/car_stopping_shorter_version.mp4'
fvs = FileVideoStream(input_video_path).start()
time.sleep(1.0)
fps = FPS().start()
in_holder_path, op_holder_path = return_io_frames_holder()
print(in_holder_path, op_holder_path)
# loop over frames from the video file stream
input_frames_list = []
while fvs.more():
frame_id += 1
frame = fvs.read()
if (frame is None):
continue
cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()), (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
cv2.imwrite(f"{in_holder_path}/{frame_id}.jpeg", frame)
# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
fvs.stop()
colonel_batch(in_holder_path, op_holder_path, batch_size,
async_workers_count)
#op_holder_path = "/home/gr8-adakron/Adakron_bay/work/FastAPI/fastapi_exp/video_processing/outputs/bCe7mEE/"
write_detected_video(output_video_path, op_holder_path)
def main():
# Get ROI from frames
file_name = "/home/smart/WBC286 InvL-Pillars -35mbar 15fps 29-11-2019 v3.4.avi"
#file_name = "C:/Users/Me/Desktop/capstone/WBC286 InvL-Pillars -350mbar 150fps 29-11-2019 v3.4.avi"
cap = FileVideoStream(file_name).start()
image = cap.read()
print("***** PROCESSING ROI for RUN 1 ***** File: %s" % file_name)
cap.stop()
print("***** PROCESSING RUN 1 ***** File: %s" % file_name)
print("Frame size: ", image.shape)
Channels = 34
#original size: [502,1402,3]
# r = [84, 357, 1238, 130]
r = [
int(0.167 * image.shape[0]),
int(0.25 * image.shape[1]),
int(0.883 * image.shape[1]), 130
]
x1, x2, y1, y2, sub_ch, channel_len = to_crop(image, r, Channels)
# run count
run_standard = standard(file_name, Channels)
(fps) = run_standard.standard_run(x1, x2, y1, y2, sub_ch, channel_len)
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
rbc_counts = run_standard.rbc_detection()
run_standard.process_results(rbc_counts)
print(
"----------------------------------------------------------------------"
)
class VideoSource (object):
def __init__ (self, video_file, log, use_pi_camera = True, resolution=(320, 200), framerate = 30, night = False):
self.filename = video_file
self.log = log
self.use_pi_camera = use_pi_camera
self.resolution = resolution
self.framerate = framerate
self.night = night
self.fvs = None
self.stream = None
self._done = False
def start (self):
if self.filename is not None:
self.log.debug('Video file: %s', self.filename)
self.fvs = FileVideoStream(self.filename).start()
self.stream = self.fvs.stream
time.sleep(1.0)
else:
if self.use_pi_camera:
self.log.debug('Pi Camera (%d %d)', self.resolution[0], self.resolution[1])
self.stream = VideoStream(src=0,
usePiCamera=True,
resolution=self.resolution,
framerate=self.framerate,
sensor_mode=5
).start()
else:
self.log.debug('Web Camera')
self.stream = cv2.VideoCapture(0)
self.stream.set(cv2.CAP_PROP_BUFFERSIZE, 2)
self.resolution = (
self.stream.get(cv2.CAP_PROP_FRAME_WIDTH),
self.stream.get(cv2.CAP_PROP_FRAME_HEIGHT)
)
self.framerate = self.stream.get(cv2.CAP_PROP_FPS)
return self.resolution, self.framerate
def read(self):
if self.filename:
frame = self.fvs.read()
else:
frame = self.stream.read()
return frame
def stop (self):
if self.filename:
self.fvs.stop()
else:
self.stream.stop()
self._done = True
def done(self):
# check to see if video is still running
running = (self.filename and self.fvs.running()) or True
self._done = not running
return self._done
def read_frames(self):
# start the file video stream thread and allow the buffer to
# start to fill
print("[INFO] starting video file thread...")
args = self.argument_parser()
output_path = self.cwd / Path(args.output_dir)
output_path.mkdir(parents=True, exist_ok=True)
fvs = FileVideoStream(args.video).start()
time.sleep(1.0)
# start the FPS timer
fps = FPS().start()
detector = mtcnn.MTCNN()
fno = 0
#frames = VideoMeta(args.video).fps()
#print(meta)
e = TfPoseEstimator(get_graph_path("mobilenet_thin"),
target_size=(432, 368))
# loop over frames from the video file stream
while fvs.more():
fno += 1
print(fno)
# grab the frame from the threaded video file stream, resize
# it, and convert it to grayscale (while still retaining 3
# channels)
frame = fvs.read()
#if (fno % frames != 0):
# continue
try:
frame = imutils.resize(frame, width=432, height=368)
except:
break
#frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#frame = np.dstack([frame, frame, frame])
humans = e.inference(frame)
image = TfPoseEstimator.draw_humans(frame, humans, imgcopy=False)
print("humans:", humans)
# display the size of the queue on the frame
# cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()),
# (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
# show the frame and update the FPS counter
cv2.imshow("Frame", image)
cv2.waitKey(1)
fps.update()
# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
fvs.stop()
def run(self, filepath):
detector = dlib.get_frontal_face_detector()
# predictor = dlib.shape_predictor(os.path.join(sys.path[0], "data/")) #ignore for now
# Start the frame queue
vs = FileVideoStream(filepath)
if (not vs.stream.isOpened()):
return 0, 0
self.maxframes = int(vs.stream.get(cv2.CAP_PROP_FRAME_COUNT))
vs.start()
best_frame = 0
scoremax = 0
self.framecounter = 0
# loop over the frames from the video stream
while (self.framecounter < self.maxframes):
# grab the frame from the threaded video stream, resize it to
# have a maximum width of 400 pixels, and convert it to
# grayscale
frame = vs.read()
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
# detect faces in the grayscale frame
rects, scores, idx = detector.run(gray, 0, 0)
# loop over the face detections
self.framecounter = self.framecounter + 1
for i, rect in enumerate(rects):
# compute the bounding box of the face
(bX, bY, bW, bH) = face_utils.rect_to_bb(rect)
# keep track of the highest scored face
if (scores[i] > scoremax):
scoremax = scores[i]
best_face = frame[bY:bY + bH, bX:bX + bW]
best_frame = self.framecounter
#cv2.imshow("BEST_FACE", best_face)
# if the q key was pressed, break from the loop
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
# Stop the frame queue
vs.stop()
if (best_frame == 0):
#If no face was found
best_face = 0
else:
# Swap the R,B values
best_face = cv2.cvtColor(best_face, cv2.COLOR_BGR2RGB)
return best_face, best_frame
def detect_blinks(videoFilePath):
frameCount = 0
blinkCount = 0
faceDetector = dlib.get_frontal_face_detector()
landmarksPredictor = dlib.shape_predictor(shapePredictorPath)
leftEyeIdx = face_utils.FACIAL_LANDMARKS_IDXS['left_eye']
rightEyeIdx = face_utils.FACIAL_LANDMARKS_IDXS['right_eye']
if FILE_VIDEO_STREAM:
vs = FileVideoStream(videoFilePath).start()
else:
vs = VideoStream().start()
time.sleep(0.5)
target_face_save_path = os.path.join(CURRENT_FOLDER_PATH,
str(time.time()) + '.jpg')
try:
while True:
if FILE_VIDEO_STREAM and not vs.more():
break
frame = vs.read()
frame = rotate(frame, 90)
frame = imutils.resize(frame, width=500)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
target_face = gray
faces = faceDetector(gray, 0)
for (i, face) in enumerate(faces):
(x, y, w, h) = face_utils.rect_to_bb(face)
landmarks = landmarksPredictor(gray, face)
landmarks = face_utils.shape_to_np(landmarks)
leftEye = landmarks[leftEyeIdx[0]:leftEyeIdx[1]]
rightEye = landmarks[rightEyeIdx[0]:rightEyeIdx[1]]
leftEAR = calculateEAR(leftEye)
rightEAR = calculateEAR(rightEye)
ear = (leftEAR + rightEAR) / 2.0
if ear < EAR_THRESHOLD:
frameCount += 1
else:
target_face = gray
if frameCount >= EAR_CONSEC_FRAMES:
blinkCount += 1
frameCount = 0
cv2.imwrite(target_face_save_path, target_face)
except:
blinkCount = 0
vs.stop()
return blinkCount, target_face_save_path
def fry_gif(update, url, number_of_cycles, args):
number_of_emojis = 1.5 if args['high-fat'] else 1 if args['low-fat'] else 0
bulge_probability = 0.3 if args['heavy'] else 0.15 if args['light'] else 0
magnitude = 4 if args['deep'] else 1 if args['shallow'] else 2
name = update.message.from_user.first_name
filename = '%s_%s_%s' % (update.message.chat_id, name,
update.message.message_id)
filepath = bin_path + '/temp/' + filename
caption = __get_caption(name, number_of_cycles, args)
output = bin_path + '/temp/out_' + filename + '.mp4'
gifbio = BytesIO()
gifbio.name = filename + '.gif'
fs = [__posterize, __sharpen, __increase_contrast, __colorize]
shuffle(fs)
if not __download_gif(url, filepath):
return
fvs = FileVideoStream(filepath + '.mp4').start()
frame = fvs.read()
height, width, _ = frame.shape
try:
fps = fvs.get(CAP_PROP_FPS)
except:
fps = 30
out = VideoWriter(output, VideoWriter_fourcc(*'mp4v'), fps,
(width, height))
out.write(
fry_frame(frame, number_of_cycles, fs, number_of_emojis,
bulge_probability, magnitude, args))
while fvs.more() or fvs.more():
try:
temp = fry_frame(fvs.read(), number_of_cycles, fs,
number_of_emojis, bulge_probability, magnitude,
args)
except Exception as e:
break
out.write(temp)
fvs.stop()
fvs.stream.release()
out.release()
update.message.reply_animation(open(output, 'rb'),
caption=caption,
quote=True)
try:
__upload_to_imgur(output, caption)
except (Exception, BaseException) as e:
print(e)
remove(filepath + '.mp4')
def process_video(analyzed_videos, reference_digits, time_parsable, ts_box, vdir, video):
print("[*] Processing video {} from {}".format(video, vdir.directory))
if video in analyzed_videos:
print("[*] Video has been processed. Checking if processing is complete...")
last_processed_frame = get_last_processed_frame(video)
print("[*] Last processed frame for this video is: ", last_processed_frame)
else:
last_processed_frame = None
vs = FileVideoStream(os.path.join(vdir.directory, video)).start()
# Reset frame number
f_num = 0
# Allow the buffer some time to fill
time.sleep(2.0)
while vs.more():
frame = vs.read()
f_num += 1
if (last_processed_frame is not None) and (f_num <= last_processed_frame):
print("[*] Current frame is {}. Waiting for {}...".format(f_num, last_processed_frame + 1))
# Give video buffer time to fill so we don't overtake it
time.sleep(0.01)
continue
else:
try:
# Process the timestamp area in the video
frame_time, ts_box = compute_frame_time(frame, reference_digits, time_parsable, ts_box)
except AttributeError:
if frame is None:
print("[!] Frame was none.")
break
print("Something went wrong. Trying again...")
continue
if frame_time is not None:
time_parsable = True
else:
print("[!] Failed to process time, probably because the frame is distorted.")
# Add the frame information to the logging database
add_frame(directory=vdir,
video=video,
time=frame_time,
frame_number=f_num)
print("{} is done being processed. Adding to database of processed videos...".format(video))
add_processed_video(video=video, total_frames=f_num)
vs.stop()
def video_extract_features(vid_path, frame_number, end_frame):
# FASTER video reading (decode in separate thread)
fvs = FileVideoStream(vid_path, start_frame=frame_number).start()
time.sleep(1.0)
current_frame = frame_number - 1
frameCount = end_frame - frame_number + 1
roi = [0.25, 0.55, 0.35, 0.65]
frame_width = int(fvs.get_width())
frame_height = int(fvs.get_height())
result_width = round(roi[3] * frame_width) - round(roi[2] * frame_width)
result_height = round(roi[1] * frame_height) - round(roi[0] * frame_height)
buf = np.empty((frameCount, result_height, result_width), np.dtype('uint8'))
buf_canny = np.empty((frameCount, result_height, result_width), np.dtype('uint8'))
hist_buf = np.empty((frameCount, 16))
print_iter = 0
while fvs.more():
print_iter += 1
# Capture frame-by-frame
frame = fvs.read()
current_frame += 1
frame_roi = get_ROI(frame, roi)
harris_result = get_harris_feature(frame_roi)
hist_result = extract_frame_histogram(frame_roi)
# canny_result = get_canny_feature(frame_roi)
buf[current_frame - frame_number] = harris_result
hist_buf[current_frame - frame_number] = hist_result
# buf_canny[current_frame - frame_number] = canny_result
# cv2.imshow('Frame', harris_result)
if divmod(print_iter, 60)[1] == 0:
print(f'Progress: {100*(current_frame-frame_number)/(end_frame-frame_number)}%')
# Press Q on keyboard to exit
if cv2.waitKey(10) & 0xFF == ord('q'):
print(f'current_frame: {current_frame}')
break
if current_frame == end_frame:
fvs.stop()
break
#save numpy matrix of feature frames
np.save('library_match_1_segment_harris.npy', buf)
np.save('library_match_1_segment_histogram.npy', hist_buf)
def run_video(path, net):
'''Runs the uploaded video through the juggle counter algorithm
'''
confidence = 0.45
(W, H) = (200, 200)
direction = 1
bounces = 0
prev_cY = 0
frame_num = 0
prev_frame_num = -7
first_detection = True
print("Processing video...")
vs = FileVideoStream(path).start()
while vs.more():
frames_per_read = 2
for i in range(frames_per_read):
frame = vs.read()
frame_num += 1
try:
frame = cv2.resize(frame, (W, H))
except cv2.error as e:
print("...end of video")
break
detection = process_frame(frame, net, W, H)
if detection[0, 0, 0, 2] > confidence:
(cX, cY, bounces, direction, prev_cY, prev_frame_num,
diameter_temp) = check_detection(frame_num, prev_frame_num, frame,
detection, W, H, bounces,
direction, prev_cY)
if first_detection:
diameter = diameter_temp
first_detection = False
cv2.putText(frame, str(bounces), (int(W * 0.86), int(W * 0.2)),
cv2.FONT_HERSHEY_SIMPLEX, int(W * 0.007), (0, 255, 0),
2)
if 0.9 * diameter < diameter_temp < 1.1 * diameter:
cv2.circle(frame, (cX, cY), int(W * 0.03), (0, 255, 0), -1)
cv2.imshow("Frame", frame)
key = cv2.waitKey(1) & 0xFF
# Only for debuggin:
# if the `q` key was pressed, break from the loop
if key == ord("q"):
break
cv2.destroyAllWindows()
vs.stop()
return bounces
def main():
filepath = "Raw Video Output 10x Inv-L.avi"
#Load frames into memory
get_frames = cuda_video_stream.video_queue(filepath, 0, 0, 934,
239).start()
stream = FileVideoStream(filepath, queue_size=500).start()
# Benchmark tests
with Timer("OpenCV"):
output1 = cv2_bgsub(stream)
stream.stop()
with Timer("OpenCV with CUDA"):
output2 = cuda_bgsub(get_frames)
stream = FileVideoStream(filepath).start()
with Timer("Numba test"):
output = numba_test(stream)
def realtime(src, modelweightspath):
if (type(src) == str):
cap = FileVideoStream(src).start()
elif (type(src) == int):
cap = WebcamVideoStream(src).start() # for fast fps read for real-time
fps = FPS().start()
#cap = cv2.VideoCapture(0) # for cv2 read
h = 512
w = 384
seg_model = load_bonnet(3, h, w)
seg_model.load_weights(modelweightspath)
check = True
while check:
start = time.time()
#ret,frame = cap.read() # for cv2 read
frame = cap.read() # for fast fps read webcamvideostream
#print(frame.shape) # frame is of: BGR type
frame = cv2.resize(frame, (w, h), interpolation=cv2.INTER_AREA)
cv2.imshow('Input', frame)
print(frame.shape)
IP = []
ip = load_input(frame, h, w)
IP.append(ip)
IP = np.array(IP)
pred = seg_model.predict(IP)
prediction = pred.argmax(axis=-1)
prediction = to_categorical(prediction, 3)
prediction = np.reshape(prediction, (h, w, 3))
prediction = prediction[:, :, [2, 1, 0]]
cv2.imshow('Output', prediction) # 2=> R 0=> B frame => BGR
end = time.time()
fps.update()
print("FrameRate:" + str(1 / (end - start)))
c = cv2.waitKey(1)
if c == 27:
break
if (type(src) == str
): # Check if next frame exists in case of video file
check = cap.more()
fps.stop()
print("AVg FrameRate:" + str(fps.fps()))
cap.stop() # for fast fps read
#cap.release() # for cv2 read
cv2.destroyAllWindows()
def run_detection(fast_mtcnn, filename):
frames = []
frames_processed = 0
faces_detected = 0
batch_size = 60
start = time.time()
v_cap = FileVideoStream(filename).start()
v_len = int(v_cap.stream.get(cv2.CAP_PROP_FRAME_COUNT))
for j in range(v_len):
frame = v_cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
if len(frames) >= batch_size or j == v_len - 1:
faces, boxes = fast_mtcnn(frames)
frames_processed += len(frames)
faces_detected += len(faces)
frames = []
fps = frames_processed / (time.time() - start)
print(
f' Frames per second: {fps:.3f},',
f'faces detected: {faces_detected}\r',
end=''
)
print("========================")
print(boxes[0])
print(faces_detected)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR )
for boxNo in range(faces_detected):
xStart = int(boxes[0][boxNo, 0])
yStart = int(boxes[0][boxNo, 1])
xEnd = int(boxes[0][boxNo, 2])
yEnd = int(boxes[0][boxNo, 3])
# [[148.05846 93.21905 217.33783 175.8501 ]]
print( xStart, yStart, xEnd, yEnd )
cv2.rectangle(frame,
(xStart, yStart),
(xEnd, yEnd),
(255, 255, 255),
thickness=1)
cv2.rectangle(frame,(0,0),(550,30),(0, 0, 0),-1)
label = "Number of faces detected : {:} Frames per second : {:.3f}".format(faces_detected, fps)
cv2.putText(frame, label, (20,20),cv2.FONT_HERSHEY_SIMPLEX,0.5,(255, 255, 255),1)
cv2.imshow("Result",frame)
cv2.waitKey(0)
v_cap.stop()
cv2.destroyAllWindows()
def vplay():
stream = askopenfilename()
ld = AdvancedLaneDetectorWithMemory(opts, ipts, src_pts, dst_pts, 10, 50, 25)
# import the necessary packages
print("[INFO] starting video file thread...")
fvs = FileVideoStream(stream).start()
time.sleep(1.0)
# start the FPS timer
fps = FPS().start()
# loop over frames from the video file stream
while fvs.more():
# grab the frame from the threaded video file stream, resize
# it, and convert it to grayscale (while still retaining 3
# channels)
frame = fvs.read()
frame = imutils.resize(frame,width = 450)
frame = ld.process_image(frame)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
#frame = np.dstack([frame])
frame = np.dstack([frame, frame, frame])
# display the size of the queue on the frame
# show the frame and update the FPS counter
frame = imutils.resize(frame,width = 900)
cv2.imshow("Frame", frame)
cv2.waitKey(1)
fps.update()
# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
fvs.stop()
def num_blinks(video):
vs = FileVideoStream(video).start()
fileStream = True
COUNTER = 0
TOTAL = 0
time.sleep(1.0)
best_frame = None
max_ear = -1
while True:
if fileStream and not vs.more():
break
frame = vs.read()
frame = imutils.resize(frame, width=450)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
rects = detector(gray, 0)
for rect in rects:
shape = predictor(gray, rect)
shape = face_utils.shape_to_np(shape)
leftEye = shape[lStart:lEnd]
rightEye = shape[rStart:rEnd]
leftEAR = eye_aspect_ratio(leftEye)
rightEAR = eye_aspect_ratio(rightEye)
ear = (leftEAR + rightEAR) / 2.0
print ear
if ear > max_ear:
best_frame = gray
max_ear = ear
if ear < EYE_AR_THRESH:
COUNTER += 1
else:
if COUNTER >= EYE_AR_CONSEC_FRAMES:
TOTAL += 1
COUNTER = 0
vs.stop()
return TOTAL, best_frame
# grab the frame from the threaded video file stream, resize
# it, and convert it to grayscale (while still retaining 3
# channels)
frame = fvs.read()
# Relocated filtering into producer thread with transform=filterFrame
# Python 2.7: FPS 92.11 -> 131.36
# Python 3.7: FPS 41.44 -> 50.11
#frame = filterFrame(frame)
# display the size of the queue on the frame
cv2.putText(frame, "Queue Size: {}".format(fvs.Q.qsize()),
(10, 30), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 255, 0), 2)
# show the frame and update the FPS counter
cv2.imshow("Frame", frame)
cv2.waitKey(1)
if fvs.Q.qsize() < 2: # If we are low on frames, give time to producer
time.sleep(0.001) # Ensures producer runs now, so 2 is sufficient
fps.update()
# stop the timer and display FPS information
fps.stop()
print("[INFO] elasped time: {:.2f}".format(fps.elapsed()))
print("[INFO] approx. FPS: {:.2f}".format(fps.fps()))
# do a bit of cleanup
cv2.destroyAllWindows()
fvs.stop()