def plot_images()

in torchbenchmark/models/yolov3/yolo_utils/utils.py [0:0]

• 58 lines of code
• 25 McCabe index (conditional complexity)

def plot_images(images, targets, paths=None, fname='images.jpg', names=None, max_size=640, max_subplots=16):
    tl = 3  # line thickness
    tf = max(tl - 1, 1)  # font thickness
    if os.path.isfile(fname):  # do not overwrite
        return None

    if isinstance(images, torch.Tensor):
        images = images.cpu().numpy()

    if isinstance(targets, torch.Tensor):
        targets = targets.cpu().numpy()

    # un-normalise
    if np.max(images[0]) <= 1:
        images *= 255

    bs, _, h, w = images.shape  # batch size, _, height, width
    bs = min(bs, max_subplots)  # limit plot images
    ns = np.ceil(bs ** 0.5)  # number of subplots (square)

    # Check if we should resize
    scale_factor = max_size / max(h, w)
    if scale_factor < 1:
        h = math.ceil(scale_factor * h)
        w = math.ceil(scale_factor * w)

    # Empty array for output
    mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8)

    # Fix class - colour map
    prop_cycle = plt.rcParams['axes.prop_cycle']
    # https://stackoverflow.com/questions/51350872/python-from-color-name-to-rgb
    hex2rgb = lambda h: tuple(int(h[1 + i:1 + i + 2], 16) for i in (0, 2, 4))
    color_lut = [hex2rgb(h) for h in prop_cycle.by_key()['color']]

    for i, img in enumerate(images):
        if i == max_subplots:  # if last batch has fewer images than we expect
            break

        block_x = int(w * (i // ns))
        block_y = int(h * (i % ns))

        img = img.transpose(1, 2, 0)
        if scale_factor < 1:
            img = cv2.resize(img, (w, h))

        mosaic[block_y:block_y + h, block_x:block_x + w, :] = img
        if len(targets) > 0:
            image_targets = targets[targets[:, 0] == i]
            boxes = xywh2xyxy(image_targets[:, 2:6]).T
            classes = image_targets[:, 1].astype('int')
            gt = image_targets.shape[1] == 6  # ground truth if no conf column
            conf = None if gt else image_targets[:, 6]  # check for confidence presence (gt vs pred)

            boxes[[0, 2]] *= w
            boxes[[0, 2]] += block_x
            boxes[[1, 3]] *= h
            boxes[[1, 3]] += block_y
            for j, box in enumerate(boxes.T):
                cls = int(classes[j])
                color = color_lut[cls % len(color_lut)]
                cls = names[cls] if names else cls
                if gt or conf[j] > 0.3:  # 0.3 conf thresh
                    label = '%s' % cls if gt else '%s %.1f' % (cls, conf[j])
                    plot_one_box(box, mosaic, label=label, color=color, line_thickness=tl)

        # Draw image filename labels
        if paths is not None:
            label = os.path.basename(paths[i])[:40]  # trim to 40 char
            t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]
            cv2.putText(mosaic, label, (block_x + 5, block_y + t_size[1] + 5), 0, tl / 3, [220, 220, 220], thickness=tf,
                        lineType=cv2.LINE_AA)

        # Image border
        cv2.rectangle(mosaic, (block_x, block_y), (block_x + w, block_y + h), (255, 255, 255), thickness=3)

    if fname is not None:
        mosaic = cv2.resize(mosaic, (int(ns * w * 0.5), int(ns * h * 0.5)), interpolation=cv2.INTER_AREA)
        cv2.imwrite(fname, cv2.cvtColor(mosaic, cv2.COLOR_BGR2RGB))

    return mosaic