Python cv2.FONT_HERSHEY_SIMPLEX Examples

def draw_boxes_frame(frame, frame_size, boxes_dicts, class_names, input_size):
  """Draws detected boxes in a video frame"""
  boxes_dict = boxes_dicts[0]
  resize_factor = (frame_size[0] / input_size[1], frame_size[1] / input_size[0])
  for cls in range(len(class_names)):
    boxes = boxes_dict[cls]
    color = (0, 0, 255)
    if np.size(boxes) != 0:
      for box in boxes:
        xy = box[:4]
        xy = [int(xy[i] * resize_factor[i % 2]) for i in range(4)]
        cv2.rectangle(frame, (xy[0], xy[1]), (xy[2], xy[3]), color[::-1], 2)
        (test_width, text_height), baseline = cv2.getTextSize(class_names[cls],
                                                              cv2.FONT_HERSHEY_SIMPLEX,
                                                              0.75, 1)
        cv2.rectangle(frame,
                      (xy[0], xy[1]),
                      (xy[0] + test_width, xy[1] - text_height - baseline),
                      color[::-1],
                      thickness=cv2.FILLED)
        cv2.putText(frame, class_names[cls], (xy[0], xy[1] - baseline), cv2.FONT_HERSHEY_SIMPLEX, 0.75, (0, 0, 0), 1) 

def vis_class(img, pos, class_str, bg_color):
    """Visualizes the class."""
    font_color = cfg.VIS.SHOW_CLASS.COLOR
    font_scale = cfg.VIS.SHOW_CLASS.FONT_SCALE

    x0, y0 = int(pos[0]), int(pos[1])
    # Compute text size.
    txt = class_str
    font = cv2.FONT_HERSHEY_SIMPLEX
    ((txt_w, txt_h), _) = cv2.getTextSize(txt, font, font_scale, 1)
    # Place text background.
    back_tl = x0, y0 - int(1.3 * txt_h)
    back_br = x0 + txt_w, y0
    cv2.rectangle(img, back_tl, back_br, bg_color, -1)
    # Show text.
    txt_tl = x0, y0 - int(0.3 * txt_h)
    cv2.putText(img, txt, txt_tl, font, font_scale, font_color, lineType=cv2.LINE_AA)

    return img 

def draw_box_with_label(frame, x_min, y_min, x_max, y_max, label, info, thickness=2, color=None, position='ul'):
    if color is None:
        color = (0,0,255)
    display_text = "{}: {}".format(label, info)
    cv2.rectangle(frame, (x_min, y_min), (x_max, y_max), color, thickness)
    font_scale = 0.5
    font = cv2.FONT_HERSHEY_SIMPLEX
    # get the width and height of the text box
    size = cv2.getTextSize(display_text, font, fontScale=font_scale, thickness=2)
    text_width = size[0][0]
    text_height = size[0][1]
    line_height = text_height + size[1]
    # set the text start position
    if position == 'ul':
        text_offset_x = x_min
        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
    elif position == 'ur':
        text_offset_x = x_max - (text_width+8)
        text_offset_y = 0 if y_min < line_height else y_min - (line_height+8)
    elif position == 'bl':
        text_offset_x = x_min
        text_offset_y = y_max
    elif position == 'br':
        text_offset_x = x_max - (text_width+8)
        text_offset_y = y_max
    # make the coords of the box with a small padding of two pixels
    textbox_coords = ((text_offset_x, text_offset_y), (text_offset_x + text_width + 2, text_offset_y + line_height))
    cv2.rectangle(frame, textbox_coords[0], textbox_coords[1], color, cv2.FILLED)
    cv2.putText(frame, display_text, (text_offset_x, text_offset_y + line_height - 3), font, fontScale=font_scale, color=(0, 0, 0), thickness=2) 

def draw():
    filenames = [os.path.splitext(f)[0] for f in glob.glob("for_task3/*.txt")]
    txt_files = [s + ".txt" for s in filenames]
    for txt in txt_files:
        image = cv2.imread('test_original/'+ txt.split('/')[1].split('.')[0]+'.jpg', cv2.IMREAD_COLOR)
        with open(txt, 'r') as txt_file:
            for line in csv.reader(txt_file):
                box = [int(string, 10) for string in line[0:8]]
                if len(line) < 9:
                    print(txt)
                cv2.rectangle(image, (box[0], box[1]), (box[4], box[5]), (0,255,0), 2)
                font = cv2.FONT_HERSHEY_SIMPLEX
                cv2.putText(image, line[8].upper(), (box[0],box[1]), font, 0.5, (0, 0, 255), 1, cv2.LINE_AA)
        cv2.imwrite('task2_result_draw/'+ txt.split('/')[1].split('.')[0]+'.jpg', image) 

def draw_rpn_boxes(img, img_name, boxes, scores, im_scale, nms, save_dir):
    """
    :param boxes: [(x1, y1, x2, y2)]
    """
    boxes = recover_scale(boxes, im_scale)

    base_name = img_name.split('/')[-1]
    color = (0, 255, 0)
    out = img.copy()

    if nms:
        boxes, scores = TextDetector.pre_process(boxes, scores)
        file_name = "%s_rpn_nms.jpg" % base_name
    else:
        file_name = "%s_rpn.jpg" % base_name

    for i, box in enumerate(boxes):
        cv2.rectangle(out, (box[0], box[1]), (box[2], box[3]), color, 2)
        cx = int((box[0] + box[2]) / 2)
        cy = int((box[1] + box[3]) / 2)
        cv2.putText(out, "%.01f" % scores[i], (cx, cy), cv2.FONT_HERSHEY_SIMPLEX, 0.2, (255, 0, 0))

    cv2.imwrite(os.path.join(save_dir, file_name), out) 

def draw_value_reward_score(self, img, value, reward, score):
    img = img.copy()
    # Average with 0.5 for semi-transparent background
    img[:14] = img[:14] * 0.5 + 0.25
    img[50:] = img[50:] * 0.5 + 0.25
    if self.cfg.gan == 'ls':
      red = -np.tanh(float(score) / 1) * 0.5 + 0.5
    else:
      red = -np.tanh(float(score) / 10.0) * 0.5 + 0.5
    top = '%+.2f %+.2f' % (value, reward)
    cv2.putText(img, top, (3, 7), cv2.FONT_HERSHEY_SIMPLEX, 0.25,
                (1.0, 1.0 - red, 1.0 - red))
    score = '%+.3f' % score
    cv2.putText(img, score, (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.35,
                (1.0, 1.0 - red, 1.0 - red))
    return img 

def label_images(images, labels):
    font = cv.FONT_HERSHEY_SIMPLEX
    new_imgs = []
    for i, img in enumerate(images):
        new_img = ((img.copy() + 1.) * 127.5).astype(np.uint8)
        if new_img.shape[-1] == 3:
            new_img = new_img[..., ::-1]
            new_img = cv.resize(new_img, (100, 100), interpolation=cv.INTER_LINEAR)
            new_img = cv.putText(new_img, str(labels[i]), (10, 30), font, 1, (255, 255, 255), 2, cv.LINE_AA)
            new_img = cv.copyMakeBorder(new_img, top=2, bottom=2, left=2, right=2, borderType=cv.BORDER_CONSTANT,
                                        value=(255, 255, 255))
        else:
            new_img = np.squeeze(new_img)
            new_img = cv.resize(new_img, (100, 100), interpolation=cv.INTER_LINEAR)
            new_img = cv.putText(new_img, str(labels[i]), (10, 30), font, 1, (255), 2, cv.LINE_AA)
            new_img = new_img[..., None]

        new_img = (new_img / 127.5 - 1.0).astype(np.float32)
        new_imgs.append(new_img[..., ::-1])
    return np.stack(new_imgs, axis=0) 

def add_coco_bbox(self, bbox, cat, conf=1, show_txt=True, img_id='default'): 
        bbox = np.array(bbox, dtype=np.int32)
        # cat = (int(cat) + 1) % 80
        cat = int(cat)
        # print('cat', cat, self.names[cat])
        c = self.colors[cat][0][0].tolist()
        if self.theme == 'white':
            c = (255 - np.array(c)).tolist()
        txt = '{}{:.1f}'.format(self.names[cat], conf)
        font = cv2.FONT_HERSHEY_SIMPLEX
        cat_size = cv2.getTextSize(txt, font, 0.5, 2)[0]
        cv2.rectangle(
          self.imgs[img_id], (bbox[0], bbox[1]), (bbox[2], bbox[3]), c, 2)
        if show_txt:
            cv2.rectangle(self.imgs[img_id],
                        (bbox[0], bbox[1] - cat_size[1] - 2),
                        (bbox[0] + cat_size[0], bbox[1] - 2), c, -1)
            cv2.putText(self.imgs[img_id], txt, (bbox[0], bbox[1] - 2), 
                      font, 0.5, (0, 0, 0), thickness=1, lineType=cv2.LINE_AA) 

def vis_images_and_indexs(images, features, dir, name):
  # indexs = np.reshape(indexs, (len(indexs),))
  # print('visualizing images and indexs: ', images.shape, indexs.shape)
  id_imgs = []
  for feature in features:
    img = np.ones((64, 64, 3))
    cv2.putText(img,
                str(feature), (4, 33), cv2.FONT_HERSHEY_SIMPLEX, 0.25,
                (1.0, 0.0, 0.0))
    id_imgs.append(img)
  id_imgs = np.stack(id_imgs, axis=0)
  # print('id imgs: ', id_imgs.shape)

  vis_imgs = np.vstack([images, id_imgs])
  image = make_image_grid(vis_imgs, per_row=images.shape[0])
  vis_dir = dir
  try:
    os.mkdir(vis_dir)
  except:
    pass
  cv2.imwrite(os.path.join(vis_dir, name + '.png'), image[:, :, ::-1] * 255.0) 

def main():
    fp = 'macbookPro.jpg'
    # image = Image.open(fp)
    # image.show()
    image = cv2.imread(fp)
    barcodes = decode(image)
    decoded = barcodes[0]
    print(decoded)
    #
    url: bytes = decoded.data
    url = url.decode()
    print(url)
    # rect
    rect = decoded.rect
    print(rect)  # Rect(left=19, top=19, width=292, height=292)

    # loop over the detected barcodes
    for barcode in barcodes:
        # extract the bounding box location of the barcode and draw the
        # bounding box surrounding the barcode on the image
        (x, y, w, h) = barcode.rect
        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)

        # the barcode data is a bytes object so if we want to draw it on
        # our output image we need to convert it to a string first
        barcodeData = barcode.data.decode("utf-8")
        barcodeType = barcode.type

        # draw the barcode data and barcode type on the image
        text = "{} ({})".format(barcodeData, barcodeType)
        cv2.putText(image, text, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX,
                    0.5, (0, 0, 255), 2)

        # print the barcode type and data to the terminal
        print("[INFO] Found {} barcode: {}".format(barcodeType, barcodeData))

    # show the output image
    cv2.imshow("Image", image)
    # cv2.imwrite('macbook_qr_rect.jpg', image)
    cv2.waitKey(0)  # 按任意键退出 

def show_who_in_image(self, path, get_face: bool = True, show: bool = True, turn_rgb: bool = True):
		min_im, image, all_frames = self.index_image(path, get_face)

		for (confidance, who), frame in zip(min_im, all_frames):
			try:
				color = self.colors[str(who)]
				x1, x2, y1, y2 = frame
				cv2.rectangle(image, (x1, y1), (x2, y2), color, 4)
				cv2.putText(image, f"id: {str(who)}- conf:{abs(round(float(confidance), 2))}", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 3, cv2.LINE_AA) # -{round(float(confidance), 2)}
			except KeyError:
				continue

		if turn_rgb:
			image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

		if show:
			cv2.imshow("a", image)
			cv2.waitKey(1)

		return image, min_im, all_frames 

def overlay_class_names(self, image, predictions):
        """
        Adds detected class names and scores in the positions defined by the
        top-left corner of the predicted bounding box

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `scores` and `labels`.
        """
        scores = predictions.get_field("scores").tolist()
        labels = predictions.get_field("labels").tolist()
        labels = [self.CATEGORIES[i] for i in labels]
        boxes = predictions.bbox

        template = "{}: {:.2f}"
        for box, score, label in zip(boxes, scores, labels):
            x, y = box[:2]
            s = template.format(label, score)
            cv2.putText(
                image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
            )

        return image 

def create_text_mask(text, font_face=cv2.FONT_HERSHEY_SIMPLEX, font_scale=2, thickness=2):
    """Build a binary image with text drawn in it"""
    color = [1]

    (w, h), _ = cv2.getTextSize(
        text, fontFace=font_face, fontScale=font_scale, thickness=thickness
    )
    border = 30
    dst = np.zeros((h + border, w + border), dtype='uint8')
    cv2.putText(
        dst, text=text, org=(border // 2, h + border // 2),
        fontFace=font_face, fontScale=font_scale,
        thickness=thickness, color=color
    )

    ymask = dst.any(1).cumsum()
    ymask = (ymask != 0) & (ymask != ymask[-1])
    xmask = dst.any(0).cumsum()
    xmask = (xmask != 0) & (xmask != xmask[-1])
    dst = dst[ymask][:, xmask]

    return dst.astype(bool) 

def test_yellow_white_thresh_images(src, dst, y_low=(10,50,0), y_high=(30,255,255), w_low=(180,180,180), w_high=(255,255,255)):
	"""
	apply the thresh to images in a src folder and output to dst foler
	"""
	image_files = glob.glob(src+"*.jpg")
	for idx, file in enumerate(image_files):
		print(file)
		img = mpimg.imread(file)
		image_threshed = yellow_white_thresh(img, y_low, y_high, w_low, w_high)
		
		file_name = file.split("\\")[-1]
		print(file_name)
		out_image = dst+file_name
		print(out_image)
		# convert  binary to RGB, *255, to visiual, 1 will not visual after write to file
		image_threshed = cv2.cvtColor(image_threshed*255, cv2.COLOR_GRAY2RGB)
		
		# HSV = cv2.cvtColor(img, cv2.COLOR_RGB2HSV)
		# V = HSV[:,:,2]
		# brightness = np.mean(V)
		# info_str = "brightness is: {}".format(int(brightness))
		# cv2.putText(image_threshed, info_str, (50,700), cv2.FONT_HERSHEY_SIMPLEX,2,(0,255,255),2)
		
		cv2.imwrite(out_image, image_threshed) 

def _draw_boxes_to_image(im, res):
    colors = [(86, 0, 240), (173, 225, 61), (54, 137, 255),\
              (151, 0, 255), (243, 223, 48), (0, 117, 255),\
              (58, 184, 14), (86, 67, 140), (121, 82, 6),\
              (174, 29, 128), (115, 154, 81), (86, 255, 234)]
    font = cv2.FONT_HERSHEY_SIMPLEX
    image = np.copy(im)
    cnt = 0
    for ind, r in enumerate(res):
        if r['dets'] is None: continue
        dets = r['dets']
        for i in range(0, dets.shape[0]):
            (x1, y1, x2, y2, score) = dets[i, :]
            cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), colors[ind % len(colors)], 2)
            text = '{:s} {:.2f}'.format(r['class'], score)
            cv2.putText(image, text, (x1, y1), font, 0.6, colors[ind % len(colors)], 1)
            cnt = (cnt + 1)
    return image 

def _draw_on_image(img, objs, class_sets_dict):
    colors = [(86, 0, 240), (173, 225, 61), (54, 137, 255),\
              (151, 0, 255), (243, 223, 48), (0, 117, 255),\
              (58, 184, 14), (86, 67, 140), (121, 82, 6),\
              (174, 29, 128), (115, 154, 81), (86, 255, 234)]
    font = cv2.FONT_HERSHEY_SIMPLEX
    for ind, obj in enumerate(objs):
        if obj['box'] is None: continue
        x1, y1, x2, y2 = obj['box'].astype(int)
        cls_id = class_sets_dict[obj['class']]
        if obj['class'] == 'dontcare':
            cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), (255, 0, 0), 1)
            continue
        cv2.rectangle(img, (int(x1), int(y1)), (int(x2), int(y2)), colors[cls_id % len(colors)], 1)
        text = '{:s}*|'.format(obj['class'][:3]) if obj['difficult'] == 1 else '{:s}|'.format(obj['class'][:3])
        text += '{:.1f}|'.format(obj['truncation'])
        text += str(obj['occlusion'])
        cv2.putText(img, text, (x1-2, y2-2), font, 0.5, (255, 0, 255), 1)
    return img 

def overlay_class_names(self, image, predictions):
        """
        Adds detected class names and scores in the positions defined by the
        top-left corner of the predicted bounding box

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `scores` and `labels`.
        """
        scores = predictions.get_field("scores").tolist()
        labels = predictions.get_field("labels").tolist()
        labels = [self.CATEGORIES[i] for i in labels]
        boxes = predictions.bbox

        template = "{}: {:.2f}"
        for box, score, label in zip(boxes, scores, labels):
            x, y = box[:2]
            s = template.format(label, score)
            cv2.putText(
                image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
            )

        return image 

def overlay_class_names(self, image, predictions):
        """
        Adds detected class names and scores in the positions defined by the
        top-left corner of the predicted bounding box

        Arguments:
            image (np.ndarray): an image as returned by OpenCV
            predictions (BoxList): the result of the computation by the model.
                It should contain the field `scores` and `labels`.
        """
        scores = predictions.get_field("scores").tolist()
        labels = predictions.get_field("labels").tolist()
        labels = [self.CATEGORIES[i] for i in labels]
        boxes = predictions.bbox

        template = "{}: {:.2f}"
        for box, score, label in zip(boxes, scores, labels):
            x, y = box[:2]
            s = template.format(label, score)
            cv2.putText(
                image, s, (x, y), cv2.FONT_HERSHEY_SIMPLEX, .5, (255, 255, 255), 1
            )

        return image 

def ProcessFrame(self, frame):
        # segment arm region
        segment = self.SegmentArm(frame)

        # make a copy of the segmented image to draw on
        draw = cv2.cvtColor(segment, cv2.COLOR_GRAY2RGB)

        # draw some helpers for correctly placing hand
        cv2.circle(draw,(self.imgWidth/2,self.imgHeight/2),3,[255,102,0],2)       
        cv2.rectangle(draw, (self.imgWidth/3,self.imgHeight/3), (self.imgWidth*2/3, self.imgHeight*2/3), [255,102,0],2)

        # find the hull of the segmented area, and based on that find the
        # convexity defects
        [contours,defects] = self.FindHullDefects(segment)

        # detect the number of fingers depending on the contours and convexity defects
        # draw defects that belong to fingers green, others red
        [nofingers,draw] = self.DetectNumberFingers(contours, defects, draw)

        # print number of fingers on image
        cv2.putText(draw, str(nofingers), (30,30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255))
        return draw 

def show_who_in_image(self, path, get_face: bool = True, show: bool = True, turn_rgb: bool = True):
		min_im, image, all_frames = self.detect_which(path, get_face)

		for (confidance, who), frame in zip(min_im, all_frames):
			color = self.colors[who]
			x1, x2, y1, y2 = frame
			cv2.rectangle(image, (x1, y1), (x2, y2), color, 4)
			cv2.putText(image, f"{who}", (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 3, cv2.LINE_AA) # -{round(float(confidance), 2)}

		if turn_rgb:
			image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

		if show:
			cv2.imshow("a", image)
			cv2.waitKey(0)

		return image 

def _draw_boxes_to_image(im, res):
    colors = [(86, 0, 240), (173, 225, 61), (54, 137, 255),\
              (151, 0, 255), (243, 223, 48), (0, 117, 255),\
              (58, 184, 14), (86, 67, 140), (121, 82, 6),\
              (174, 29, 128), (115, 154, 81), (86, 255, 234)]
    font = cv2.FONT_HERSHEY_SIMPLEX
    image = np.copy(im)
    cnt = 0
    for ind, r in enumerate(res):
        if r['dets'] is None: continue
        dets = r['dets']
        for i in range(0, dets.shape[0]):
            (x1, y1, x2, y2, score) = dets[i, :]
            cv2.rectangle(image, (int(x1), int(y1)), (int(x2), int(y2)), colors[ind % len(colors)], 2)
            text = '{:s} {:.2f}'.format(r['class'], score)
            cv2.putText(image, text, (x1, y1), font, 0.6, colors[ind % len(colors)], 1)
            cnt = (cnt + 1)
    return image 

def render_bbox(image, annotations):
  """Renders a visualzation of bounding box annotations.

  Args:
    image: numpy array of image bytes.
    annotations: the annotations to render onto the image.

  Returns:
    numpy array of image, with bounding box drawn.
  """
  height, width, _ = image.shape
  for annotation in annotations:
    x0, y0, x1, y1 = (annotation.bbox.left, annotation.bbox.top,
                      annotation.bbox.right, annotation.bbox.bottom)
    # Converts coordinates from relative [0,1] space to [width,height]
    x0, y0, x1, y1 = int(x0 * width), int(y0 * height), int(x1 * width), int(
        y1 * height)
    percent = int(100 * annotation.confidence_score)
    label = '%d%% %s' % (percent, annotation.class_name)

    if annotation.track_id == -1:
      color = (0, 255, 0)
    else:
      color = colors[annotation.track_id % len(colors)]

    image = cv2.rectangle(image, (x0, y0), (x1, y1), color, 2)
    image = cv2.putText(image, label, (x0, y0 + 30), cv2.FONT_HERSHEY_SIMPLEX,
                        1.0, (255, 0, 0), 2)
  return image 

def draw_result(self, img, result):
        for i in range(len(result)):
            x = int(result[i][1])
            y = int(result[i][2])
            w = int(result[i][3] / 2)
            h = int(result[i][4] / 2)
            cv2.rectangle(img, (x - w, y - h), (x + w, y + h), (0, 255, 0), 2)
            cv2.rectangle(img, (x - w, y - h - 20),
                          (x + w, y - h), (125, 125, 125), -1)
            lineType = cv2.LINE_AA if cv2.__version__ > '3' else cv2.CV_AA
            cv2.putText(
                img, result[i][0] + ' : %.2f' % result[i][5],
                (x - w + 5, y - h - 7), cv2.FONT_HERSHEY_SIMPLEX, 0.5,
                (0, 0, 0), 1, lineType) 

def camera(self):
        cap = cv2.VideoCapture(0)
        if not cap.isOpened():
            print("Unable to open camera")
            exit(-1)
        count_tfps = 1
        accum_time = 0
        curr_fps = 0
        fps = "FPS: ??"
        prev_time = timer()
        while True:
            res, img = cap.read()
            curr_time = timer()
            exec_time = curr_time - prev_time
            prev_time = curr_time
            accum_time = accum_time + exec_time
            curr_fps = curr_fps + 1

            if accum_time > 1:
                accum_time = accum_time - 1
                fps = curr_fps
                curr_fps = 0
            if res:
                show_image = self.process(img=img)
                cv2.putText(
                    show_image, "FPS: " + str(fps), (10,  20),
                    cv2.FONT_HERSHEY_SIMPLEX, 0.9, (204, 51, 51), 2
                )

                cv2.imshow("Detection", show_image)
                k = cv2.waitKey(1)
                if k == 27:
                    break
            else:
                print("Unable to read image")
                exit(-1)
            count_tfps += 1
        cap.release()
        cv2.destroyAllWindows() 

def __init__(self, vid_file):  # vid_file = 'videos/traffic.avi'
        self.cnt_up = 0
        self.cnt_down = 0
        self.zone1 = (100, 200)
        self.zone2 = (450, 100)

        self.cap = cv2.VideoCapture(vid_file)  # insane

        # Capture the properties of VideoCapture to console
        # for i in range(19):
        #     print(i, self.cap.get(i))

        self.w = self.cap.get(3)
        self.h = self.cap.get(4)
        self.frameArea = self.h * self.w
        self.areaTH = self.frameArea / 200
        print('Area Threshold', self.areaTH)

        # Input/Output Lines
        self.line_up = int(2 * (self.h / 5))
        self.line_down = int(3 * (self.h / 5))

        self.up_limit = int(1 * (self.h / 5))
        self.down_limit = int(4 * (self.h / 5))

        self.line_down_color = (255, 0, 0)
        self.line_up_color = (0, 0, 255)
        self.pt1 = [0, self.line_down]
        self.pt2 = [self.w, self.line_down]
        self.pts_L1 = np.array([self.pt1, self.pt2], np.int32)
        self.pts_L1 = self.pts_L1.reshape((-1, 1, 2))
        self.pt3 = [0, self.line_up]
        self.pt4 = [self.w, self.line_up]
        self.pts_L2 = np.array([self.pt3, self.pt4], np.int32)
        self.pts_L2 = self.pts_L2.reshape((-1, 1, 2))

        self.pt5 = [0, self.up_limit]
        self.pt6 = [self.w, self.up_limit]
        self.pts_L3 = np.array([self.pt5, self.pt6], np.int32)
        self.pts_L3 = self.pts_L3.reshape((-1, 1, 2))
        self.pt7 = [0, self.down_limit]
        self.pt8 = [self.w, self.down_limit]
        self.pts_L4 = np.array([self.pt7, self.pt8], np.int32)
        self.pts_L4 = self.pts_L4.reshape((-1, 1, 2))

        # Create the background subtractor
        self.fgbg = cv2.createBackgroundSubtractorMOG2()

        self.kernelOp = np.ones((3, 3), np.uint8)
        self.kernelOp2 = np.ones((5, 5), np.uint8)
        self.kernelCl = np.ones((11, 11), np.uint8)

        # Variables
        self.font = cv2.FONT_HERSHEY_SIMPLEX
        self.vehicles = []
        self.max_p_age = 5
        self.pid = 1 

def visualize_bbox(img, bbox, class_id, class_idx_to_name, color=BOX_COLOR, thickness=2):
    x_min, y_min, x_max, y_max = bbox
    x_min, x_max, y_min, y_max = int(x_min), int(x_max), int(y_min), int(y_max)
    cv2.rectangle(img, (x_min, y_min), (x_max, y_max),
                  color=color, thickness=thickness)
    # class_name = class_idx_to_name[class_id]
    # ((text_width, text_height), _) = cv2.getTextSize(class_name, cv2.FONT_HERSHEY_SIMPLEX, 0.35, 1)
    # cv2.rectangle(img, (x_min, y_min - int(1.3 * text_height)), (x_min + text_width, y_min), BOX_COLOR, -1)
    # cv2.putText(img, class_name, (x_min, y_min - int(0.3 * text_height)), cv2.FONT_HERSHEY_SIMPLEX, 0.35,TEXT_COLOR, lineType=cv2.LINE_AA)
    return img 

def display_colorname(bgrcap, p):
    """Display the colornames on the webcam picture."""
    p = p.astype(np.uint16)
    _, col = getcolor(p)
    if col in ('blue', 'green', 'red'):
        txtcol = (255, 255, 255)
    else:
        txtcol = (0, 0, 0)
    font = cv2.FONT_HERSHEY_SIMPLEX
    tz = cv2.getTextSize(col, font, 0.4, 1)[0]
    cv2.putText(
        bgrcap, col, tuple(p - (tz[0] // 2, -tz[1] // 2)), font, 0.4, txtcol, 1) 

def plot_boxes_cv2(img, boxes, savename=None, class_names=None, color=None):
    import cv2
    colors = torch.FloatTensor([[1,0,1],[0,0,1],[0,1,1],[0,1,0],[1,1,0],[1,0,0]])
    def get_color(c, x, max_val):
        ratio = float(x)/max_val * 5
        i = int(math.floor(ratio))
        j = int(math.ceil(ratio))
        ratio = ratio - i
        r = (1-ratio) * colors[i][c] + ratio*colors[j][c]
        return int(r*255)

    width = img.shape[1]
    height = img.shape[0]
    for i in range(len(boxes)):
        box = boxes[i]
        x1 = int(round((box[0] - box[2]/2.0) * width))
        y1 = int(round((box[1] - box[3]/2.0) * height))
        x2 = int(round((box[0] + box[2]/2.0) * width))
        y2 = int(round((box[1] + box[3]/2.0) * height))

        if color:
            rgb = color
        else:
            rgb = (255, 0, 0)
        if len(box) >= 7 and class_names:
            cls_conf = box[5]
            cls_id = box[6]
            #print('%s: %f' % (class_names[cls_id], cls_conf))
            classes = len(class_names)
            offset = cls_id * 123457 % classes
            red   = get_color(2, offset, classes)
            green = get_color(1, offset, classes)
            blue  = get_color(0, offset, classes)
            if color is None:
                rgb = (red, green, blue)
            img = cv2.putText(img, class_names[cls_id], (x1,y1), cv2.FONT_HERSHEY_SIMPLEX, 1.2, rgb, 1)
        img = cv2.rectangle(img, (x1,y1), (x2,y2), rgb, 1)
    if savename:
        print("save plot results to %s" % savename)
        cv2.imwrite(savename, img)
    return img 

def dpkAnnotator(dpkini,annotationfile):

    config = ConfigParser()
    configFile = str(dpkini)
    config.read(configFile)
    project_path = config.get('general DPK settings', 'project_folder')
    annotationsPath = annotationfile
    bodyPartsListPath = os.path.join(project_path, 'skeleton.csv')

    app = Annotator(datapath=annotationsPath, dataset='images', skeleton=bodyPartsListPath, shuffle_colors=False, text_scale=1)
    im = np.zeros((300, 600, 3))
    cv2.putText(im, 'Instructions', (10, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(255, 255, 255), 2)
    cv2.putText(im, '+- = rescale image by +/- 10%', (10, 40), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, 'left mouse button = move active keypoint to cursor location', (10, 60), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, 'WASD = move active keypoint 1px or 10px', (10, 80), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, 'JL = next or previous image', (10, 100), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, '<> = jump 10 images forward or backward', (10, 120), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, 'I,K or tab, shift+tab = switch active keypoint', (10, 140), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, 'R = mark image as unannotated ("reset")', (10, 160), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, 'F = mark image as annotated ("finished")', (10, 180), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, 'esc or Q = quit', (10, 200), cv2.FONT_HERSHEY_SIMPLEX, 0.5,(0, 255, 0), 2)
    cv2.putText(im, 'Tap tab to begin', (10, 240), cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
    cv2.imshow('Instructions', im)
    k = cv2.waitKey(0)
    while (1):
        cv2.imshow('Instructions', im)
        k = cv2.waitKey(0)
        app.run()
        if k == 27:  # Esc key to stop
            print('Annotatations saved in: ' + str(annotationfile))
            break 

def draw_all_result(self, image):
        """Draw the detection result on image"""
        for facebox, conf in self.detection_result:
            cv2.rectangle(image, (facebox[0], facebox[1]),
                          (facebox[2], facebox[3]), (0, 255, 0))
            label = "face: %.4f" % conf
            label_size, base_line = cv2.getTextSize(
                label, cv2.FONT_HERSHEY_SIMPLEX, 0.5, 1)

            cv2.rectangle(image, (facebox[0], facebox[1] - label_size[1]),
                          (facebox[0] + label_size[0],
                           facebox[1] + base_line),
                          (0, 255, 0), cv2.FILLED)
            cv2.putText(image, label, (facebox[0], facebox[1]),
                        cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0))