Spaces:
Running
Running
File size: 4,670 Bytes
dd5b9d2 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 |
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()
|