Python colormap.colormap方法代码示例

# 需要导入模块: from utils import colormap [as 别名]
# 或者: from utils.colormap import colormap [as 别名]
def vis_parsing(img, parsing, colormap, show_segms=True):
    """Visualizes a single binary parsing."""
    img = img.astype(np.float32)
    idx = np.nonzero(parsing)

    parsing_alpha = cfg.VIS.SHOW_PARSS.PARSING_ALPHA
    colormap = colormap_utils.dict2array(colormap)
    parsing_color = colormap[parsing.astype(np.int)]

    border_color = cfg.VIS.SHOW_PARSS.BORDER_COLOR
    border_thick = cfg.VIS.SHOW_PARSS.BORDER_THICK

    img[idx[0], idx[1], :] *= 1.0 - parsing_alpha
    # img[idx[0], idx[1], :] += alpha * parsing_color
    img += parsing_alpha * parsing_color

    if cfg.VIS.SHOW_PARSS.SHOW_BORDER and not show_segms:
        _, contours, _ = cv2.findContours(parsing.copy(), cv2.RETR_CCOMP, cv2.CHAIN_APPROX_NONE)
        cv2.drawContours(img, contours, -1, border_color, border_thick, cv2.LINE_AA)

    return img.astype(np.uint8) 

# 需要导入模块: from utils import colormap [as 别名]
# 或者: from utils.colormap import colormap [as 别名]
def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None and len(segms) > 0:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

# 需要导入模块: from utils import colormap [as 别名]
# 或者: from utils.colormap import colormap [as 别名]
def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im 

# 需要导入模块: from utils import colormap [as 别名]
# 或者: from utils.colormap import colormap [as 别名]
def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, tracks=None, thresh=0.9,
        kp_thresh=2, show_box=False, dataset=None, show_class=False,
        linewidth=1):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, tracks, classes = convert_from_cls_format(
            boxes, segms, keypoints, tracks)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return

    if segms is not None:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]),
                track_id=tracks[i] if tracks is not None and len(tracks) > i else -1,
                thick=linewidth)

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # show keypoints
        if keypoints is not None and len(keypoints) > i:
            im = vis_keypoints(im, keypoints[i], kp_thresh, linewidth=linewidth)

    return im 

# 需要导入模块: from utils import colormap [as 别名]
# 或者: from utils.colormap import colormap [as 别名]
def vis_one_image_opencv(
        im, boxes, segms=None, keypoints=None, thresh=0.9, kp_thresh=2,
        show_box=False, dataset=None, show_class=False):
    """Constructs a numpy array with the detections visualized."""

    if isinstance(boxes, list):
        boxes, segms, keypoints, classes = convert_from_cls_format(
            boxes, segms, keypoints)

    if boxes is None or boxes.shape[0] == 0 or max(boxes[:, 4]) < thresh:
        return im

    if segms is not None:
        masks = mask_util.decode(segms)
        color_list = colormap()
        mask_color_id = 0

    # Display in largest to smallest order to reduce occlusion
    areas = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1])
    sorted_inds = np.argsort(-areas)

    for i in sorted_inds:
        bbox = boxes[i, :4]
        score = boxes[i, -1]
        if score < thresh:
            continue

        # show box (off by default)
        if show_box:
            im = vis_bbox(
                im, (bbox[0], bbox[1], bbox[2] - bbox[0], bbox[3] - bbox[1]))

        # show class (off by default)
        if show_class:
            class_str = get_class_string(classes[i], score, dataset)
            im = vis_class(im, (bbox[0], bbox[1] - 2), class_str)

        # show mask
        if segms is not None and len(segms) > i:
            color_mask = color_list[mask_color_id % len(color_list), 0:3]
            mask_color_id += 1
            im = vis_mask(im, masks[..., i], color_mask)

        # # show keypoints
        # if keypoints is not None and len(keypoints) > i:
        #     im = vis_keypoints(im, keypoints[i], kp_thresh)

    return im