How to use the imutils.video.FPS function in imutils

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github jeffbass / imagezmq / tests / timing_receive_images.py View on Github external
from collections import defaultdict
from imutils.video import FPS
import imagezmq

# instantiate image_hub
image_hub = imagezmq.ImageHub()

image_count = 0
sender_image_counts = defaultdict(int)  # dict for counts by sender
first_image = True

try:
    while True:  # receive images until Ctrl-C is pressed
        sent_from, image = image_hub.recv_image()
        if first_image:
            fps = FPS().start()  # start FPS timer after first image is received
            first_image = False
        fps.update()
        image_count += 1  # global count of all images received
        sender_image_counts[sent_from] += 1  # count images for each RPi name
        cv2.imshow(sent_from, image)  # display images 1 window per sent_from
        cv2.waitKey(1)
        image_hub.send_reply(b"OK")  # REP reply
except (KeyboardInterrupt, SystemExit):
    pass  # Ctrl-C was pressed to end program; FPS stats computed below
except Exception as ex:
    print('Python error with no Exception handler:')
    print('Traceback error:', ex)
    traceback.print_exc()
finally:
    # stop the timer and display FPS information
    print()
github kendricktan / cv-lane / cv / fast_fps_video.py View on Github external
# created a *threaded *video stream, allow the camera sensor to warmup,
# and start the FPS counter
vs = PiVideoStream(resolution=(CAMERA_WIDTH, CAMERA_HEIGHT))

# Camera settings
vs.camera.shutter_speed = SHUTTER
vs.exposure = EXPOSURE
vs.camera.awb_mode = AWB_MODE
vs.camera.awb_gains = AWB_GAINS

# Start camera
vs.start()
time.sleep(2.0)

fps = FPS().start()

# loop over some frames...this time using the threaded 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()

    # check to see if the frame should be displayed to our screen
    if args["display"] > 0:
        cv2.imshow("Frame", frame)
        key = cv2.waitKey(1) & 0xFF

    # update the FPS counter
    fps.update()

    # Key press
github eridgd / fast-neural-style-ncs / webcam.py View on Github external
if frame is not None:
            break

    frame_resize = cv2.resize(frame, None, fx=1 / args.downsample, fy=1 / args.downsample)
    img_shape = frame_resize.shape
    fast_style = FastStyle(args.checkpoint, img_shape, args.device)

    if args.video_out is not None:
        fourcc = cv2.VideoWriter_fourcc(*'XVID')
        if args.concat:
            shp = (int(2*img_shape[1]*args.scale),int(img_shape[0]*args.scale))
        else:
            shp = (int(img_shape[1]*args.scale),int(img_shape[0]*args.scale))
        out = cv2.VideoWriter(args.video_out, fourcc, args.fps, shp)

    fps = FPS().start()

    count = 0

    while(True):
        ret, frame = cap.read()

        if ret is True:       
            if args.zoom > 1:
                o_h, o_w, _ = frame.shape
                frame = cv2.resize(frame, None, fx=args.zoom, fy=args.zoom)
                h, w, _ = frame.shape
                off_h, off_w = int((h - o_h) / 2), int((w - o_w) / 2)
                frame = frame[off_h:h-off_h, off_w:w-off_w, :]

            # resize image and detect face
            frame_resize = cv2.resize(frame, None, fx=1 / args.downsample, fy=1 / args.downsample)
github nullbyte91 / Intel-edge-AI-foundation-udacity / optimzation_tricks / main_async.py View on Github external
infer_network.load_model(args.model, args.device, CPU_EXTENSION, num_requests=2)

    current_inference, next_inference = 0, 1

    # Get a Input blob shape
    in_n, in_c, in_h, in_w = infer_network.get_input_shape()

    # Get a output blob name
    _ = infer_network.get_output_name()
    
    # Handle the input stream
    cap = cv2.VideoCapture(args.input)
    cap.open(args.input)
    _, frame = cap.read()

    fps = FPS().start()
    # Process frames until the video ends, or process is exited
    while cap.isOpened():
        # Read the next frame
        flag, frame = cap.read()
        if not flag:
            break
        
        fh = frame.shape[0]
        fw = frame.shape[1]
        key_pressed = cv2.waitKey(60)

        # Pre-process the frame
        image_resize = cv2.resize(frame, (in_w, in_h))
        image = image_resize.transpose((2,0,1))
        image = image.reshape(in_n, in_c, in_h, in_w)
github perrytsao / pc-drone / utility / fps_demo.py View on Github external
import imutils
import cv2
import timeit

# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-n", "--num-frames", type=int, default=100,
	help="# of frames to loop over for FPS test")
ap.add_argument("-d", "--display", type=int, default=-1,
	help="Whether or not frames should be displayed")
args = vars(ap.parse_args())

# grab a pointer to the video stream and initialize the FPS counter
print("[INFO] sampling frames from webcam...")
stream = cv2.VideoCapture(1)
fps = FPS().start()
tic=timeit.default_timer()
font = cv2.FONT_HERSHEY_SIMPLEX
# loop over some frames
while fps._numFrames < args["num_frames"]:
	# grab the frame from the stream and resize it to have a maximum
	# width of 400 pixels
	(grabbed, frame) = stream.read()
	frame = imutils.resize(frame, width=400)

	# check to see if the frame should be displayed to our screen
	if args["display"] > 0:
		toc=timeit.default_timer()   
		cv2.putText(frame, "%0.3f" % (toc-tic), (50,200), font, 2, (255,255,255),4,cv2.LINE_AA)       
		cv2.imshow("Frame", frame)
		key = cv2.waitKey(1) & 0xFF
github kendricktan / cv-lane / cv / EyeCanSee.py View on Github external
def calculate_fps(self, frames_no=100):
        fps = FPS().start()

        # Don't wanna display window
        if self.debug:
            self.debug = not self.debug

        for i in range(0, frames_no):
            self.where_lane_be()
            fps.update()

        fps.stop()

        # Don't wanna display window
        if not self.debug:
            self.debug = not self.debug

        print('Time taken: {:.2f}'.format(fps.elapsed()))
github nimotli / SmartAssistantArab / VideoDetect.py View on Github external
# load the pre-trained EAST text detector
print("[INFO] loading EAST text detector...")
net = cv2.dnn.readNet(args["east"])

# if a video path was not supplied, grab the reference to the web cam
if not args.get("video", False):
	print("[INFO] starting video stream...")
	vs = VideoStream(src=0).start()
	time.sleep(1.0)
 
# otherwise, grab a reference to the video file
else:
	vs = cv2.VideoCapture(args["video"])
 
# start the FPS throughput estimator
fps = FPS().start()

# loop over frames from the video stream
while True:
	# grab the current frame, then handle if we are using a
	# VideoStream or VideoCapture object
	frame = vs.read()
	frame = frame[1] if args.get("video", False) else frame
 
	# check to see if we have reached the end of the stream
	if frame is None:
		break
 
	# resize the frame, maintaining the aspect ratio
	frame = imutils.resize(frame, width=1000)
	orig = frame.copy()
github datitran / face2face-demo / generate_train_data.py View on Github external
def main():
    os.makedirs('original', exist_ok=True)
    os.makedirs('landmarks', exist_ok=True)

    cap = cv2.VideoCapture(args.filename)
    fps = video.FPS().start()

    count = 0
    while cap.isOpened():
        ret, frame = cap.read()

        frame_resize = cv2.resize(frame, None, fx=1 / DOWNSAMPLE_RATIO, fy=1 / DOWNSAMPLE_RATIO)
        gray = cv2.cvtColor(frame_resize, cv2.COLOR_BGR2GRAY)
        faces = detector(gray, 1)
        black_image = np.zeros(frame.shape, np.uint8)

        t = time.time()

        # Perform if there is a face detected
        if len(faces) == 1:
            for face in faces:
                detected_landmarks = predictor(gray, face).parts()
github rrqq / eye_of_sauron / real-time-object-detection / real_time_object_detection.py View on Github external
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

# initialize the video stream, allow the cammera sensor to warmup,
# and initialize the FPS counter
print("[INFO] starting video stream...")
url = 'd.mp4'
#vs = cv2.VideoCapture('http://d3tj01z94i74qz.cloudfront.net/cam0/videos/cam0_30_fps.mp4')
# vs = cv2.VideoCapture(url)

vs = VideoStream(url).start()

time.sleep(1.0)
fps = FPS().start()

# 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()

    if frame is None:
        print("End of stream..")
        break
    # et, frame = vs.read()

    frame = imutils.resize(frame, width=400)

    # grab the frame dimensions and convert it to a blob
    (h, w) = frame.shape[:2]

imutils

A series of convenience functions to make basic image processing functions such as translation, rotation, resizing, skeletonization, displaying Matplotlib images, sorting contours, detecting edges, and much more easier with OpenCV and both Python 2.7 and Python 3.

MIT
Latest version published 4 years ago

Package Health Score

64 / 100
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