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@@ -34,7 +34,7 @@ def visualize(image, bboxes, scores, labels, class_colors, class_names):
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return image
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## Visualize the input data during the training stage
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-def vis_data(images, targets, num_classes=80, normalized_bbox=False, color_format='bgr', pixel_mean=None, pixel_std=None, box_format="xyxy"):
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+def vis_data(images, targets, num_classes=80, pixel_mean=None, pixel_std=None):
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"""
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images: (tensor) [B, 3, H, W]
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targets: (list) a list of targets
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@@ -54,31 +54,14 @@ def vis_data(images, targets, num_classes=80, normalized_bbox=False, color_forma
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# denormalize image
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if pixel_mean is not None and pixel_std is not None:
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image = image * pixel_std + pixel_mean
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-
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- if color_format == 'rgb':
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- image = image[..., (2, 1, 0)] # RGB to BGR
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-
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+
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image = image.astype(np.uint8)
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image = image.copy()
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img_h, img_w = image.shape[:2]
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# visualize target
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for box, label in zip(tgt_boxes, tgt_labels):
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- if box_format == "xyxy":
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- x1, y1, x2, y2 = box
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- elif box_format == "xywh":
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- cx, cy, bw, bh = box
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- x1 = cx - 0.5 * bw
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- y1 = cy - 0.5 * bh
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- x2 = cx + 0.5 * bw
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- y2 = cy + 0.5 * bh
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-
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- if normalized_bbox:
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- x1 *= img_w
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- y1 *= img_h
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- x2 *= img_w
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- y2 *= img_h
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-
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+ x1, y1, x2, y2 = box
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x1, y1 = int(x1), int(y1)
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x2, y2 = int(x2), int(y2)
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cls_id = int(label)
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yjh0410