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 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 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 main(_argv):
input_layer = tf.keras.layers.Input([FLAGS.size, FLAGS.size, 3])
feature_maps = YOLOv4(input_layer, NUM_CLASS)
bbox_tensors = []
for i, fm in enumerate(feature_maps):
bbox_tensor = decode(fm, NUM_CLASS, i)
bbox_tensors.append(bbox_tensor)
model = tf.keras.Model(input_layer, bbox_tensors)
utils.load_weights(model, 'data/YOLOv4-obj_1000.weights')
vid = FileVideoStream(FLAGS.input) # Reading input
return_value, frame = vid.read()
fourcc = cv2.VideoWriter_fourcc('F', 'M', 'P', '4')
out = cv2.VideoWriter(FLAGS.output, fourcc, 10.0,
(frame.shape[1], frame.shape[0]), True)
plates = []
n = 0
Sum = 0
while True:
start = time.time()
n += 1
return_value, frame = vid.read()
if frame is None:
continue
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
bboxes = plateDetect(frame, FLAGS.size,
model) # License plate detection
for i in range(len(bboxes)):
img = frame[int(bboxes[i][1]):int(bboxes[i][3]),
int(bboxes[i][0]):int(bboxes[i][2])]
prediction_groups = pipeline.recognize(
[img]) # Text detection and recognition on license plate
string = ''
for j in range(len(prediction_groups[0])):
string = string + prediction_groups[0][j][0].upper()
if platePattern(string) == True and string not in plates:
plates.append(string)
if len(plates) > 0:
drawText(frame, plates)
frame = utils.draw_bbox(
frame, bboxes) # Draws bounding box around license plate
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)
Sum += time.time() - start
print('Avg fps:- ', Sum / n)
out.write(frame)
cv2.imshow("result", frame)
if cv2.waitKey(1) == 27: break
out.release()
cv2.destroyAllWindows()
def standard_run(
self,
x1: int,
x2: int,
y1: int,
y2: int,
sub_ch: list,
channel_len,
):
fps = FPS().start()
cap = FileVideoStream(self.filename).start()
count = 0
start = time.time()
cycle_start = time.time()
frame = cap.read()
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
while cap.more():
# print(count)
frame = cap.read()
if frame is None:
break
if count < 200:
self.frames_buffer.append(frame)
frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
frame = frame[y1:y2, x1:x2]
crop = self.mask.apply(frame)
crop = cv2.GaussianBlur(crop, (7, 7), 3.0)
_, crop = cv2.threshold(crop, 150, 255,
cv2.THRESH_BINARY + cv2.THRESH_OTSU)
contours, hierarchy = cv2.findContours(crop, cv2.RETR_TREE,
cv2.CHAIN_APPROX_SIMPLE)
for i in range(len(contours)):
avg = np.mean(contours[i], axis=0)
coord = (int(avg[0][0]), int(avg[0][1])) # Coord is (x,y)
ch_pos = int(math.floor((coord[0]) / channel_len))
try:
self.sum_ch1[ch_pos] += float(1)
except:
pass
count += 1
fps.update()
fps.stop()
cycle_end = time.time()
self.cycle_count += 1
end = time.time()
detect_benchmark = end - start
print("Number of frames processed: ", count)
print("Time taken for WBC counting:", detect_benchmark)
print("[INFO] Each cycle time taken = %0.5fs" %
((cycle_end - cycle_start) / count))
return fps
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')
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(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()
class VideoCamera(object):
def __init__(self):
# Using OpenCV to capture from device 0. If you have trouble capturing
# from a webcam, comment the line below out and use a video file
# instead.
# self.video = cv2.VideoCapture(0)
# If you decide to use video.mp4, you must have this file in the folder
# as the main.py.
self.video = FileVideoStream("cropvideo.mp4").start()
def __del__(self):
self.video.release()
def get_frame(self):
image = self.video.read()
faceCascade = cv2.CascadeClassifier('haarcascade_profileface.xml')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
faces = faceCascade.detectMultiScale(gray, 1.1, 4)
# We are using Motion JPEG, but OpenCV defaults to capture raw images,
# so we must encode it into JPEG in order to correctly display the
# video stream.
for (x, y, w, h) in faces:
cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)
ret, jpeg = cv2.imencode('.jpg', image)
return jpeg.tobytes()
def eye_close(image):
path = "shape_predictor_68_face_landmarks.dat"
predictor = dlib.shape_predictor(path)
detector = dlib.get_frontal_face_detector()
EYE_AR_THRESH = 0.4
(lStart, lEnd) = face_utils.FACIAL_LANDMARKS_IDXS["left_eye"]
(rStart, rEnd) = face_utils.FACIAL_LANDMARKS_IDXS["right_eye"]
(mStart, mEnd) = face_utils.FACIAL_LANDMARKS_IDXS["mouth"]
vs = FileVideoStream(image).start()
time.sleep(2.0)
frame = vs.read()
frame = imutils.resize(frame, width=400)
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)
return leftEAR, rightEAR
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 process_video(file_name, n=1):
"""
iterate over frames of videofile
:param file_name: filename
:param n:process one out of every n frames-> default 1: take all frames
:return:list of finish frames
"""
fvs = FileVideoStream(file_name).start() #load video
cam = cv2.VideoCapture(file_name)
fps = cam.get(cv2.CAP_PROP_FPS) #get original fps of video
counter = 1
frame_list = []
teller = 0
while fvs.running():
if fvs.more():
teller += 1
frame = fvs.read()
if frame is not None:
frame_list.append(
cv2.resize(
frame,
(int(frame.shape[1] / 2), int(frame.shape[0] / 2)))
) #append frame to list and resize it: height/2, width/2
counter += 1
else:
time.sleep(2)
return (frame_list, fps)
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()
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 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(
"----------------------------------------------------------------------"
)
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 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 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 UAMS(self):
video_file = FileVideoStream(self.args["video"]).start()
time.sleep(2.0)
fps = FPS().start()
while video_file.more():
# Converting each frame to matrix of numbers
try:
self.frame = video_file.read()
gray = cv2.cvtColor(self.frame, cv2.COLOR_BGR2GRAY)
gray = np.dstack([gray, gray, gray])
except Exception as e:
print(e)
self.frame, self.faces, conf, detection, startX, y = self.cnn_op.CNNOperation(
self.frame, self.net, self.args)
try:
msg = self.calculation(startX, y)
except Exception as e:
print(e)
try:
cv2.imshow("frame-this is main frame", self.frame)
except Exception as e:
print(e)
fps.update()
key = cv2.waitKey(1) & 0xFF
if key == ord("q"):
break
fps.stop()
print("elapsed time : {:.2f}".format(fps.elapsed()))
print("FPS : {:.2f}".format(fps.fps()))
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 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 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 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 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 detect_object(frameCount):
# grab global references to the video stream, output frame, and
# lock variables
global vs, outputFrame, lock
# initialize the motion detector and the total number of frames
# read thus far
total = 0
# loop over the frames from the video stream
while True:
# grab the frame from the threaded video stream and resize it
# to have a maximum width of 400 pixels
frame = vs.read()
# time.sleep(0.05)
if frame is None:
print(
"INFO: unable to connect to a webcam, using file stream instead"
)
vs = FileVideoStream("static/test_video.mp4").start()
frame = vs.read()
continue
frame = imutils.resize(frame, width=400)
# grab the frame dimensions and convert it to a blob
(h, w) = frame.shape[:2]
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 0.007843,
(300, 300), 127.5)
# pass the blob through the network and obtain the detections and predictions
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in np.arange(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > args["confidence"]:
# extract the index of the class label from the
# `detections`, then compute the (x, y)-coordinates of
# the bounding box for the object
idx = int(detections[0, 0, i, 1])
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# draw the prediction on the frame
label = "{}: {:.2f}%".format(CLASSES[idx], confidence * 100)
cv2.rectangle(frame, (startX, startY), (endX, endY),
COLORS[idx], 2)
y = startY - 15 if startY - 15 > 15 else startY + 15
cv2.putText(frame, label, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.5, COLORS[idx], 2)
# acquire the lock, set the output frame, and release the lock
with lock:
outputFrame = frame.copy()
def frames():
camera = FileVideoStream()
time.sleep(1.0)
# loop over frames from the video file stream
while camera.more():
frame = camera.read()
yield frame
def showVideo(app):
fvs = FileVideoStream(app.vidDir).start()
time.sleep(1.0)
while fvs.more():
frame = fvs.read()
frame = cv2.resize(frame, (720, 480))
cv2.imshow("Frame", frame)
cv2.waitKey(1)
def detect_motion(frameCount):
# grab global references to the video stream, output frame, and
# lock variables
global vs, outputFrame, lock
# initialize the motion detector and the total number of frames
# read thus far
md = SingleMotionDetector(accumWeight=0.1)
total = 0
# loop over frames from the video stream
while True:
# read the next frame from the video stream, resize it,
# convert the frame to grayscale, and blur it
frame = vs.read()
time.sleep(0.05)
if frame is None:
print(
"INFO: unable to connect to a webcam, using file stream instead"
)
vs = FileVideoStream("video/test_video.mp4").start()
frame = vs.read()
continue
frame = imutils.resize(frame, width=400)
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
gray = cv2.GaussianBlur(gray, (7, 7), 0)
# grab the current timestamp and draw it on the frame
timestamp = datetime.datetime.now()
cv2.putText(frame, timestamp.strftime("%A %d %B %Y %I:%M:%S%p"),
(10, frame.shape[0] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.35,
(0, 0, 255), 1)
# if the total number of frames has reached a sufficient
# number to construct a reasonable background model, then
# continue to process the frame
if total > frameCount:
# detect motion in the image
motion = md.detect(gray)
# cehck to see if motion was found in the frame
if motion is not None:
# unpack the tuple and draw the box surrounding the
# "motion area" on the output frame
(thresh, (minX, minY, maxX, maxY)) = motion
cv2.rectangle(frame, (minX, minY), (maxX, maxY), (0, 0, 255),
2)
# update the background model and increment the total number
# of frames read thus far
md.update(gray)
total += 1
# acquire the lock, set the output frame, and release the
# lock
with lock:
outputFrame = frame.copy()
class CustomStream:
def __init__(self, src=0, use_cv2=False):
if use_cv2:
self.obj = cv2.VideoCapture(src)
elif src == 0:
self.obj = WebcamVideoStream(src)
elif src != 0:
self.obj = FileVideoStream(src)
def isOpened(self):
if isinstance(self.obj, cv2.VideoCapture):
return self.obj.isOpened()
return self.obj.stream.isOpened()
def start(self):
self.obj.start()
return self
def update(self):
self.obj.update()
def read(self):
if isinstance(self.obj, cv2.VideoCapture):
return self.obj.read()
return not self.obj.stopped, self.obj.read()
def stop(self):
self.obj.stop()
if isinstance(self.obj, cv2.VideoCapture):
self.obj.release()
else:
self.obj.stream.release()
def set(self, propId, value):
if isinstance(self.obj, cv2.VideoCapture):
self.obj.set(propId, value)
return
self.obj.stream.set(propId, value)
def get(self, propId):
if isinstance(self.obj, cv2.VideoCapture):
self.obj.get(propId)
return
return self.obj.stream.get(propId)
# start the file video stream thread and allow the buffer to
# start to fill
print("[INFO] starting video file thread...")
fvs = FileVideoStream(args["video"], transform=filterFrame).start()
time.sleep(1.0)
# start the FPS timer
fps = FPS().start()
# loop over frames from the video file stream
while fvs.running():
# 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
