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| from PIL import Image | |
| import supervision as sv | |
| import numpy as np | |
| from torch import tensor | |
| import cv2 | |
| colors = sv.ColorPalette.from_hex( | |
| [ | |
| "#a1c9f4", | |
| "#ffb482", | |
| "#8de5a1", | |
| "#ff9f9b", | |
| "#d0bbff", | |
| "#debb9b", | |
| "#fab0e4", | |
| "#cfcfcf", | |
| "#fffea3", | |
| "#b9f2f0", | |
| "#a1c9f4", | |
| "#ffb482", | |
| "#8de5a1", | |
| "#ff9f9b", | |
| "#d0bbff", | |
| "#debb9b", | |
| "#fab0e4", | |
| "#cfcfcf", | |
| "#fffea3", | |
| "#b9f2f0", | |
| ] | |
| ) | |
| text_palette = {str(idx): colors.by_idx(idx).as_hex() for idx in range(50)} | |
| def image_w_box(image,objxbox): | |
| box_annotator = sv.BoxCornerAnnotator(thickness=10, corner_length=30, color=colors) | |
| label_annotator = sv.LabelAnnotator(color=colors) | |
| mask_annotator = sv.MaskAnnotator(opacity=0.2, color=colors) | |
| xyxys = np.array([v.tolist() for boxes in objxbox.values() for v in boxes]) | |
| unique_labels = sorted(objxbox.keys()) | |
| class_id_map = dict(enumerate(unique_labels)) | |
| labels = [l for l, boxes in objxbox.items() for _ in boxes] | |
| class_id = [list(class_id_map.values()).index(label) for label in labels] | |
| masks = np.zeros((len(xyxys), image.shape[0], image.shape[1]), dtype=bool) | |
| for i, (x1, y1, x2, y2) in enumerate(xyxys): | |
| masks[i, int(y1):int(y2), int(x1):int(x2)] = labels[i] | |
| if len(xyxys) == 0: | |
| return image | |
| detections = sv.Detections( | |
| xyxy=xyxys, | |
| mask=masks, | |
| class_id=np.array(class_id), | |
| ) | |
| # Convert RGB to BGR for annotation | |
| image_bgr = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2BGR) | |
| # After annotation, convert back to RGB | |
| annotated_image = box_annotator.annotate(scene=image_bgr.copy(), detections=detections) | |
| annotated_image = label_annotator.annotate(scene=annotated_image, detections=detections, labels=labels) | |
| annotated_image = mask_annotator.annotate(scene=annotated_image, detections=detections) | |
| return cv2.cvtColor(annotated_image, cv2.COLOR_BGR2RGB) | |
| def image_w_box_cv2(image, objxbox): | |
| if not isinstance(image, np.ndarray): | |
| raise ValueError("Input image must be a NumPy array.") | |
| image_copy = image.copy() | |
| font = cv2.FONT_HERSHEY_SIMPLEX | |
| height, width, _ = image.shape | |
| font_scale = max(0.5, min(width, height) / 1000) | |
| font_thickness = max(1, int(font_scale * 2)) | |
| for label, boxes in objxbox.items(): | |
| for box in boxes: | |
| print("box", box) | |
| x1, y1, x2, y2 = map(int, box.tolist()) | |
| cv2.rectangle(image_copy, (x1, y1), (x2, y2), color=(0, 0, 255), thickness=2) | |
| label_text = f"{label}" | |
| (text_width, text_height), baseline = cv2.getTextSize( | |
| label_text, font, font_scale, font_thickness | |
| ) | |
| text_x1 = x1 | |
| text_y1 = y1 - text_height - baseline | |
| text_x2 = x1 + text_width | |
| text_y2 = y1 | |
| cv2.rectangle(image_copy, (text_x1, text_y1), (text_x2, text_y2), color=(255, 255, 255), thickness=-1) | |
| cv2.putText( | |
| image_copy, | |
| label_text, | |
| (x1, y1 - baseline), | |
| font, | |
| font_scale, | |
| color=(0, 0, 255), | |
| thickness=font_thickness, | |
| lineType=cv2.LINE_AA, | |
| ) | |
| return image_copy | |
| if __name__ == '__main__': | |
| image = Image.open("assets/demo.jpeg") | |
| objxbox = {'computer monitor': [tensor([ 169.5367, 301.8970, 3045.2866, 2145.4736], device='cuda:0')], 'lamp': [tensor([3400.5979, 981.1383, 4102.7178, 2417.0103], device='cuda:0')], 'kettle': [tensor([4435.6953, 1981.3882, 5318.8530, 2972.8535], device='cuda:0')], 'table': [tensor([3108.2896, 2602.6494, 5795.3037, 4201.5000], device='cuda:0')], 'business card': [tensor([ 751.5681, 2817.4629, 945.1781, 2976.9883], device='cuda:0')], 'dog': [tensor([2155.5217, 2504.7114, 2562.2791, 3173.9731], device='cuda:0'), tensor([1013.7704, 2669.0864, 1560.3319, 3452.0579], device='cuda:0')], 'inkpad': [tensor([ 755.5402, 2983.9380, 962.8440, 3176.2158], device='cuda:0')], 'mouse': [tensor([2752.5286, 3038.9062, 3046.8740, 3297.1704], device='cuda:0')], 'tray': [tensor([3314.1667, 2722.6509, 4805.7476, 3684.2314], device='cuda:0')], 'computer keyboard': [tensor([ 203.7615, 2907.8442, 737.0474, 3416.8616], device='cuda:0')], 'laptop': [tensor([ 525.8097, 2439.1343, 2882.1917, 4261.9614], device='cuda:0')], 'keyboard': [tensor([ 659.9836, 3511.1763, 2828.9368, 4271.0059], device='cuda:0')], 'cookie': [tensor([4638.1128, 3625.8831, 5082.5796, 4013.4021], device='cuda:0')]} | |
| image_w_box(image, objxbox).show() | |