Python cv2.COLOR_BGR2GRAY Examples

def prediction(self, image):
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        image = cv2.GaussianBlur(image, (21, 21), 0)
        if self.avg is None:
            self.avg = image.copy().astype(float)
        cv2.accumulateWeighted(image, self.avg, 0.5)
        frameDelta = cv2.absdiff(image, cv2.convertScaleAbs(self.avg))
        thresh = cv2.threshold(
                frameDelta, DELTA_THRESH, 255,
                cv2.THRESH_BINARY)[1]
        thresh = cv2.dilate(thresh, None, iterations=2)
        cnts = cv2.findContours(
                thresh.copy(), cv2.RETR_EXTERNAL,
                cv2.CHAIN_APPROX_SIMPLE)
        cnts = imutils.grab_contours(cnts)
        self.avg = image.copy().astype(float)
        return cnts 

def live_undistort(camera, camera_matrix, distortion_coefficients):
    """ Using a given calibration matrix, display the distorted, undistorted, and cropped frame"""
    scaled_camera_matrix, roi = cv2.getOptimalNewCameraMatrix(
        camera_matrix, distortion_coefficients, camera.size, 1, camera.size
    )
    while True:
        ret, frame = camera.cap.read()
        assert ret
        distorted_frame = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
        undistorted_frame = cv2.undistort(
            distorted_frame, camera_matrix, distortion_coefficients, None, scaled_camera_matrix,
        )
        roi_x, roi_y, roi_w, roi_h = roi
        cropped_frame = undistorted_frame[roi_y : roi_y + roi_h, roi_x : roi_x + roi_w]
        cv2.imshow("distorted %s" % (distorted_frame.shape,), distorted_frame)
        cv2.imshow("undistorted %s" % (undistorted_frame.shape,), undistorted_frame)
        cv2.imshow("cropped %s" % (cropped_frame.shape,), cropped_frame)
        cv2.waitKey(10) 

def clahe(filename):
    os.chdir(os.path.dirname(filename))
    print('Applying CLAHE, this might take awhile...')

    currentVideo = os.path.basename(filename)
    fileName, fileEnding = currentVideo.split('.',2)
    saveName = str('CLAHE_') + str(fileName) + str('.avi')
    cap = cv2.VideoCapture(currentVideo)
    imageWidth = int(cap.get(3))
    imageHeight = int(cap.get(4))
    fps = cap.get(cv2.CAP_PROP_FPS)
    fourcc = cv2.VideoWriter_fourcc(*'XVID')
    out = cv2.VideoWriter(saveName, fourcc, fps, (imageWidth, imageHeight), 0)
    try:
        while True:
            ret, image = cap.read()
            if ret == True:
                im = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                claheFilter = cv2.createCLAHE(clipLimit=2, tileGridSize=(16, 16))
                claheCorrecttedFrame = claheFilter.apply(im)
                out.write(claheCorrecttedFrame)
                if cv2.waitKey(10) & 0xFF == ord('q'):
                    break
            else:
                print(str('Completed video ') + str(saveName))
                break
    except:
        print('clahe not applied')
    cap.release()
    out.release()
    cv2.destroyAllWindows()
    return saveName 

def main():
	imagePath = "img.jpg"
	
	img = cv2.imread(imagePath)
	gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
	
	generate_histogram(gray)
	
	cv2.imwrite("before.jpg", gray)

	gray = cv2.equalizeHist(gray)
	
	generate_histogram(gray)
	
	cv2.imwrite("after.jpg",gray)
	
	return 0 

def test():
	"""
	read the pickle file on disk and implement undistor on image
	show the oringal/undistort image
	"""
	print("Reading the pickle file...")
	pickle_file = open("./camera_cal.p", "rb")
	dist_pickle = pickle.load(pickle_file)
	mtx = dist_pickle["mtx"]  
	dist = dist_pickle["dist"]
	pickle_file.close()

	print("Reading the sample image...")
	img = cv2.imread('corners_founded/corners_found13.jpg')
	img_size = (img.shape[1],img.shape[0])
	dst = cv2.undistort(img, mtx, dist, None, mtx)

	# dst = cv2.cvtColor(dst, cv2.COLOR_BGR2GRAY)
	# Visualize undistortion
	print("Visulize the result...")
	f, (ax1,ax2) = plt.subplots(1,2, figsize=(20,10))
	ax1.imshow(img), ax1.set_title('Original Image', fontsize=15)
	ax2.imshow(dst), ax2.set_title('Undistored Image', fontsize=15)
	plt.show() 

def movement(mat_1,mat_2):
    mat_1_gray     = cv2.cvtColor(mat_1.copy(),cv2.COLOR_BGR2GRAY)
    mat_1_gray     = cv2.blur(mat_1_gray,(blur1,blur1))
    _,mat_1_gray   = cv2.threshold(mat_1_gray,100,255,0)
    mat_2_gray     = cv2.cvtColor(mat_2.copy(),cv2.COLOR_BGR2GRAY)
    mat_2_gray     = cv2.blur(mat_2_gray,(blur1,blur1))
    _,mat_2_gray   = cv2.threshold(mat_2_gray,100,255,0)
    mat_2_gray     = cv2.bitwise_xor(mat_1_gray,mat_2_gray)
    mat_2_gray     = cv2.blur(mat_2_gray,(blur2,blur2))
    _,mat_2_gray   = cv2.threshold(mat_2_gray,70,255,0)
    mat_2_gray     = cv2.erode(mat_2_gray,np.ones((erodeval,erodeval)))
    mat_2_gray     = cv2.dilate(mat_2_gray,np.ones((4,4)))
    _, contours,__ = cv2.findContours(mat_2_gray,cv2.RETR_TREE,cv2.CHAIN_APPROX_SIMPLE)
    if len(contours) > 0:return True #If there were any movements
    return  False                    #if not


#Pedestrian Recognition Thread 

def detect_face(img_path, cc_path='../files/haarcascade_frontalface_default.xml'):
    """
    Detect the face from the image, return colored face
    """

    cc = cv2.CascadeClassifier(os.path.abspath(cc_path))
    img_path = os.path.abspath(img_path)
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = cc.detectMultiScale(gray, 1.3, 5)
    roi_color = None

    if len(faces) == 0:
        logging.exception(img_path + ': No face found')
    else:
        x,y,w,h = faces[0]
        _h, _w = compute_size(h, w)
        roi_color = img[y - _h:y + h + _h, x - _w:x + w + _w]

    return roi_color 

def main():
	#IMG PATHS
	imagePath = "test3.jpg"
	cascPath = "cascades/haarcascade_pedestrian.xml"

	pplCascade = cv2.CascadeClassifier(cascPath)
	image = cv2.imread(imagePath)
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	
	gray = normalize_grayimage(gray)
	 
	pedestrians = pplCascade.detectMultiScale(
		gray,
		scaleFactor=1.2,
		minNeighbors=10,
		minSize=(32,96),
		flags = cv2.cv.CV_HAAR_SCALE_IMAGE
	)

	print "Found {0} ppl!".format(len(pedestrians))

	#Draw a rectangle around the detected objects
	for (x, y, w, h) in pedestrians:
		cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)

	cv2.imwrite("saida.jpg", image)
	cv2.imshow("Ppl found", image)
	cv2.waitKey(0)
	
	return 0 

def test_motion():
    image = cv2.imread("./imgs/image.jpeg")
    print(image.shape)

    detector = Detector_Motion()

    image2 = cv2.imread("./imgs/image_box.jpg")
    print(image2.shape)
    assert image.shape == image2.shape
    image2 = cv2.cvtColor(image2, cv2.COLOR_BGR2GRAY)
    image2 = cv2.GaussianBlur(image2, (21, 21), 0)
    detector.avg = image2.astype(float)

    output = detector.prediction(image)
    df = detector.filter_prediction(output, image)
    image = detector.draw_boxes(image, df)
    print(df)
    assert df.shape[0] == 1

    cv2.imwrite("./imgs/outputcv.jpg", image) 

def detect_face(img_path, cc_path='../files/haarcascade_frontalface_default.xml'):
    """
    Detect the face from the image, return colored face
    """

    cc = cv2.CascadeClassifier(os.path.abspath(cc_path))
    img_path = os.path.abspath(img_path)
    img = cv2.imread(img_path)
    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = cc.detectMultiScale(gray, 1.3, 5)
    roi_color = None

    if len(faces) == 0:
        logging.exception(img_path + ': No face found')
    else:
        x,y,w,h = faces[0]
        _h, _w = compute_size(h, w)
        roi_color = img[y - _h:y + h + _h, x - _w:x + w + _w]

    return roi_color 

def _augment(self, img, r):
        old_dtype = img.dtype

        if img.ndim == 3:
            if self.rgb is not None:
                m = cv2.COLOR_RGB2GRAY if self.rgb else cv2.COLOR_BGR2GRAY
                grey = cv2.cvtColor(img.astype('float32'), m)
                mean = np.mean(grey)
            else:
                mean = np.mean(img, axis=(0, 1), keepdims=True)
        else:
            mean = np.mean(img)

        img = img * r + mean * (1 - r)
        if self.clip or old_dtype == np.uint8:
            img = np.clip(img, 0, 255)
        return img.astype(old_dtype) 

def getPeakFeatures():
    net = DecafNet()

    features = numpy.zeros((number_sequences,feature_length))
    labels = numpy.zeros((number_sequences,1))
    counter = 0
    # Maybe sort them
    for participant in os.listdir(os.path.join(data_dir,image_dir)):
        for sequence in os.listdir(os.path.join(data_dir,image_dir, participant)):
            if sequence != ".DS_Store":
                image_files = sorted(os.listdir(os.path.join(data_dir,image_dir, participant,sequence)))
                image_file = image_files[-1]
                print counter, image_file
                imarray = cv2.imread(os.path.join(data_dir,image_dir, participant,sequence,image_file))
                imarray = cv2.cvtColor(imarray,cv2.COLOR_BGR2GRAY)
                scores = net.classify(imarray, center_only=True)
                features[counter] = net.feature(feature_level)#.flatten()
                label_file = open(os.path.join(data_dir,label_dir, participant,sequence,image_file[:-4]+"_emotion.txt"))
                labels[counter] = eval(label_file.read())
                label_file.close()
                counter += 1

    numpy.save("featuresPeak5",features)
    numpy.save("labelsPeak5",labels) 

def run(self):
        while True:
            ret, frame = self.cam.read()
            frame_gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
            vis = frame.copy()

            if len(self.tracks) > 0:
                img0, img1 = self.prev_gray, frame_gray
                p0 = np.float32([tr[-1] for tr in self.tracks]).reshape(-1, 1, 2)
                p1, st, err = cv2.calcOpticalFlowPyrLK(img0, img1, p0, None, **lk_params)
                p0r, st, err = cv2.calcOpticalFlowPyrLK(img1, img0, p1, None, **lk_params)
                d = abs(p0-p0r).reshape(-1, 2).max(-1)
                good = d < 1
                new_tracks = []
                for tr, (x, y), good_flag in zip(self.tracks, p1.reshape(-1, 2), good):
                    if not good_flag:
                        continue
                    tr.append((x, y))
                    if len(tr) > self.track_len:
                        del tr[0]
                    new_tracks.append(tr)
                    cv2.circle(vis, (x, y), 2, (0, 255, 0), -1)
                self.tracks = new_tracks
                cv2.polylines(vis, [np.int32(tr) for tr in self.tracks], False, (0, 255, 0))
                draw_str(vis, (20, 20), 'track count: %d' % len(self.tracks))

            if self.frame_idx % self.detect_interval == 0:
                mask = np.zeros_like(frame_gray)
                mask[:] = 255
                for x, y in [np.int32(tr[-1]) for tr in self.tracks]:
                    cv2.circle(mask, (x, y), 5, 0, -1)
                p = cv2.goodFeaturesToTrack(frame_gray, mask = mask, **feature_params)
                if p is not None:
                    for x, y in np.float32(p).reshape(-1, 2):
                        self.tracks.append([(x, y)])


            self.frame_idx += 1
            self.prev_gray = frame_gray
            cv2.imshow('lk_track', vis)

            ch = cv2.waitKey(1)
            if ch == 27:
                break 

def get_face(detector, image, cpu=False):
    if cpu:
        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
        try:
            box = detector(image)[0]
            x1 = box.left()
            y1 = box.top()
            x2 = box.right()
            y2 = box.bottom()
            return [x1, y1, x2, y2]
        except:
            return None
    else:
        image = cv2.resize(image, None, fx=0.5, fy=0.5)
        box = detector.detect_from_image(image)[0]
        if box is None:
            return None
        return (2*box[:4]).astype(int) 

def test_heatmaps(heatmaps,img,i):
    heatmaps=heatmaps.numpy()
    #heatmaps=np.squeeze(heatmaps)
    heatmaps=heatmaps[:,:64,:]
    heatmaps=heatmaps.transpose(1,2,0)
    print('heatmap inside shape is',heatmaps.shape)
##    print('----------------here')
##    print(heatmaps.shape)
    img=img.numpy()
    #img=np.squeeze(img)
    img=img.transpose(1,2,0)
    img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#    print('heatmaps',heatmaps.shape)
    heatmaps = cv2.resize(heatmaps,(0,0), fx=4,fy=4)
#    print('heatmapsafter',heatmaps.shape)
    for j in range(0, 16):
        heatmap = heatmaps[:,:,j]
        heatmap = heatmap.reshape((256,256,1))
        heatmapimg = np.array(heatmap * 255, dtype = np.uint8)
        heatmap = cv2.applyColorMap(heatmapimg, cv2.COLORMAP_JET)
        heatmap = heatmap/255
        plt.imshow(img)
        plt.imshow(heatmap, alpha=0.5)
        plt.show()
        #plt.savefig('hmtestpadh36'+str(i)+js[j]+'.png') 

def search(self, query_keypoints, query_descriptors):
		# Initialize the dictionary of results
		results = {}

		# Loop over the book cover images
		for path in self.cover_paths:
			# Load the query image, convert it to greyscale, and extract keypoints and descriptors
			cover = cv2.imread(path)
			gray = cv2.cvtColor(cover, cv2.COLOR_BGR2GRAY)
			(keypoints, descriptors) = self.descriptor.describe(gray)

			# Determine the number of matched, inlier keypoints, and update the results
			score = self.match(query_keypoints, query_descriptors, keypoints, descriptors)
			results[path] = score

		# If matches were found, sort them
		if len(results) > 0:
			results = sorted([(v, k) for (k, v) in results.items() if v > 0], reverse=True)

		# Return the results
		return results 

def coherence_filter(img, sigma = 11, str_sigma = 11, blend = 0.5, iter_n = 4):
    h, w = img.shape[:2]

    for i in xrange(iter_n):
        print(i)

        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
        eigen = cv2.cornerEigenValsAndVecs(gray, str_sigma, 3)
        eigen = eigen.reshape(h, w, 3, 2)  # [[e1, e2], v1, v2]
        x, y = eigen[:,:,1,0], eigen[:,:,1,1]

        gxx = cv2.Sobel(gray, cv2.CV_32F, 2, 0, ksize=sigma)
        gxy = cv2.Sobel(gray, cv2.CV_32F, 1, 1, ksize=sigma)
        gyy = cv2.Sobel(gray, cv2.CV_32F, 0, 2, ksize=sigma)
        gvv = x*x*gxx + 2*x*y*gxy + y*y*gyy
        m = gvv < 0

        ero = cv2.erode(img, None)
        dil = cv2.dilate(img, None)
        img1 = ero
        img1[m] = dil[m]
        img = np.uint8(img*(1.0 - blend) + img1*blend)
    print('done')
    return img 

def test_heatmaps(heatmaps,img,i):
    heatmaps=heatmaps.numpy()
    #heatmaps=np.squeeze(heatmaps)
    heatmaps=heatmaps[:,:64,:]
    heatmaps=heatmaps.transpose(1,2,0)
    print('heatmap inside shape is',heatmaps.shape)
##    print('----------------here')
##    print(heatmaps.shape)
    img=img.numpy()
    #img=np.squeeze(img)
    img=img.transpose(1,2,0)
    img=cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
#    print('heatmaps',heatmaps.shape)
    heatmaps = cv2.resize(heatmaps,(0,0), fx=4,fy=4)
#    print('heatmapsafter',heatmaps.shape)
    for j in range(0, 16):
        heatmap = heatmaps[:,:,j]
        heatmap = heatmap.reshape((256,256,1))
        heatmapimg = np.array(heatmap * 255, dtype = np.uint8)
        heatmap = cv2.applyColorMap(heatmapimg, cv2.COLORMAP_JET)
        heatmap = heatmap/255
        plt.imshow(img)
        plt.imshow(heatmap, alpha=0.5)
        plt.show()
        #plt.savefig('hmtestpadh36'+str(i)+js[j]+'.png') 

def find_blank_rows_h(image):

    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    im_bw = np.zeros(gray_image.shape)
    im_bw[gray_image > 127] = 0
    im_bw[gray_image <= 127] = 1

    row_sum = np.sum(im_bw, axis=1)

    cum_sum = np.zeros(row_sum.shape)

    cum_sum[0] = row_sum[0]

    for i, sum in enumerate(row_sum[1:]):
        cum_sum[i+1] = cum_sum[i] + sum

    blank_rows = []
    for i, sum in enumerate(cum_sum):
        if is_blank(cum_sum, i):
            blank_rows.append(i)

    return blank_rows

# check n last rows 

def get_text(img, offset=0):
    text = pretreatment.get_text(img, offset)
    text = cv2.cvtColor(text, cv2.COLOR_BGR2GRAY)
    text = text / 255.0
    h, w = text.shape
    text.shape = (1, h, w, 1)
    return text 

def detect_from_image(self, tensor_or_path):
        image = self.tensor_or_path_to_ndarray(tensor_or_path, rgb=False)

        detected_faces = self.face_detector(cv2.cvtColor(image, cv2.COLOR_BGR2GRAY))

        if 'cuda' not in self.device:
            detected_faces = [[d.left(), d.top(), d.right(), d.bottom()] for d in detected_faces]
        else:
            detected_faces = [[d.rect.left(), d.rect.top(), d.rect.right(), d.rect.bottom()] for d in detected_faces]

        return detected_faces 

def computeCanny(surface):
    array = pygame.surfarray.pixels3d(surface)
    array = cv2.cvtColor(array, cv2.COLOR_BGR2GRAY)
    array = cv2.GaussianBlur(array, (5, 5), 0)
    array = cv2.Canny(array, 30, 150)
    return pygame.surfarray.make_surface(array) 

def computeCanny(self, surface):
        array = pygame.surfarray.pixels3d(surface)
        # print(array.shape)
        array = cv2.cvtColor(array, cv2.COLOR_BGR2GRAY)
        array = cv2.GaussianBlur(array, (5, 5), 0)
        array = cv2.Canny(array, 30, 150)
        return pygame.surfarray.make_surface(array) 

def convert_to_binary(image):

    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    im_bw = np.zeros(gray_image.shape)
    im_bw[gray_image > 127] = 0
    im_bw[gray_image <= 127] = 1

    return im_bw 

def clahe_queue(files):

    filesFound= [files]
    os.chdir(os.path.dirname(files))
    print('Applying CLAHE, this might take awhile...')

    for i in filesFound:
        currentVideo = os.path.basename(i)
        saveName = str('CLAHE_') + str(currentVideo[:-4]) + str('.avi')
        cap = cv2.VideoCapture(currentVideo)
        imageWidth = int(cap.get(3))
        imageHeight = int(cap.get(4))
        fps = cap.get(cv2.CAP_PROP_FPS)
        fourcc = cv2.VideoWriter_fourcc(*'XVID')
        out = cv2.VideoWriter(saveName, fourcc, fps, (imageWidth, imageHeight), 0)
        while True:
            ret, image = cap.read()
            if ret == True:
                im = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                claheFilter = cv2.createCLAHE(clipLimit=2, tileGridSize=(16, 16))
                claheCorrecttedFrame = claheFilter.apply(im)
                out.write(claheCorrecttedFrame)
                if cv2.waitKey(10) & 0xFF == ord('q'):
                    break
            else:
                print(str('Completed video ') + str(saveName))
                break

    cap.release()
    out.release()
    cv2.destroyAllWindows()
    return saveName 

def clahe_batch(directory):

    filesFound= []

    ########### FIND FILES ###########
    for i in os.listdir(directory):
        filesFound.append(i)

    os.chdir(directory)
    print('Applying CLAHE, this might take awhile...')

    for i in filesFound:
        currentVideo = i
        saveName = str('CLAHE_') + str(currentVideo[:-4]) + str('.avi')
        cap = cv2.VideoCapture(currentVideo)
        imageWidth = int(cap.get(3))
        imageHeight = int(cap.get(4))
        fps = cap.get(cv2.CAP_PROP_FPS)
        fourcc = cv2.VideoWriter_fourcc(*'XVID')
        out = cv2.VideoWriter(saveName, fourcc, fps, (imageWidth, imageHeight), 0)
        while True:
            ret, image = cap.read()
            if ret == True:
                im = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                claheFilter = cv2.createCLAHE(clipLimit=2, tileGridSize=(16, 16))
                claheCorrecttedFrame = claheFilter.apply(im)
                out.write(claheCorrecttedFrame)
                if cv2.waitKey(10) & 0xFF == ord('q'):
                    break
            else:
                print(str('Completed video ') + str(saveName))
                break

    cap.release()
    out.release()
    cv2.destroyAllWindows()
    return saveName 

def find_blank_rows(image, line_spacing=1):

    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    blank_rows = np.all(gray_image == 255, axis=1)

    im_bw = np.zeros(gray_image.shape)
    im_bw[blank_rows] = 255
    #gray_image[~blank_rows] = 0

    #cv2.imwrite("/home/psm2208/code/eval/test.png", im_bw)

    labeled, ncomponents = ndimage.label(im_bw)
    rows = []

    indices = np.indices(im_bw.shape).T[:, :, [1, 0]]

    line_bbs = ndimage.find_objects(labeled)
    sizes = np.array([[bb.stop - bb.start for bb in line_bb]
                      for line_bb in line_bbs])

    sizes = sizes[:,0]
    mask = (sizes > line_spacing)

    idx = np.flatnonzero(mask)

    for i in idx:
        labels = (labeled == (i+1))
        pixels = indices[labels.T]
        box = [min(pixels[:, 0]), min(pixels[:, 1]), max(pixels[:, 0]), max(pixels[:, 1])]
        rows.append(box)

    return rows 

def clahe_auto(directory):

    filesFound= []

    ########### FIND FILES ###########
    for i in os.listdir(directory):
        if i.__contains__(".mp4"):
            filesFound.append(i)

    os.chdir(directory)
    print('Applying CLAHE, this might take awhile...')

    for i in filesFound:
        currentVideo = i
        saveName = str('CLAHE_') + str(currentVideo[:-4]) + str('.avi')
        cap = cv2.VideoCapture(currentVideo)
        imageWidth = int(cap.get(3))
        imageHeight = int(cap.get(4))
        fps = cap.get(cv2.CAP_PROP_FPS)
        fourcc = cv2.VideoWriter_fourcc(*'XVID')
        out = cv2.VideoWriter(saveName, fourcc, fps, (imageWidth, imageHeight), 0)
        while True:
            ret, image = cap.read()
            if ret == True:
                im = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
                claheFilter = cv2.createCLAHE(clipLimit=2, tileGridSize=(16, 16))
                claheCorrecttedFrame = claheFilter.apply(im)
                out.write(claheCorrecttedFrame)
                if cv2.waitKey(10) & 0xFF == ord('q'):
                    break
            else:
                print(str('Completed video ') + str(saveName))
                break

    cap.release()
    out.release()
    cv2.destroyAllWindows()
    return saveName 

def find_marker(image):
	gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
	gray = cv2.GaussianBlur(gray, (5, 5), 0)
	edged = cv2.Canny(gray, 35, 125)
	(cnts, _) = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
	c = max(cnts, key = cv2.contourArea)

	# compute the bounding box of the of the paper region and return it
	return cv2.minAreaRect(c) 

def convert_to_binary(image):

    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

    im_bw = np.zeros(gray_image.shape)
    im_bw[gray_image > 127] = 0
    im_bw[gray_image <= 127] = 1

    return im_bw