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ca4beaa 9fab1c1 ca4beaa 9fab1c1 ca4beaa 9fab1c1 ca4beaa | 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 | import matplotlib.pyplot as plt
import matplotlib.patches as patches
import numpy as np
from PIL import Image, ImageFilter
def get_prompt_list(prompt: str):
return [txt.strip() for txt in prompt.split(".") if txt.strip() != ""]
def plot_groundingdino_boxes(img: Image.Image, result, figsize=(12, 8), show_scores=True):
"""
img: PIL Image
result: results[0] dict containing 'boxes', 'scores', 'labels'
"""
img_np = np.array(img)
plt.figure(figsize=figsize)
plt.imshow(img_np)
ax = plt.gca()
boxes = result["boxes"]
scores = result.get("scores", None)
labels = result.get("labels", None)
for i, box in enumerate(boxes):
x1, y1, x2, y2 = box
rect = patches.Rectangle((x1, y1), x2 - x1, y2 - y1, linewidth=2, edgecolor="red", facecolor="none")
ax.add_patch(rect)
text = ""
if labels is not None:
text += str(labels[i])
if show_scores and scores is not None:
text += f" ({scores[i]:.2f})"
if text:
ax.text(x1, y1 - 5, text, color="red", fontsize=12, bbox=dict(facecolor="black", alpha=0.5, pad=2))
plt.axis("off")
plt.show()
MASK_OVERLAY_RGBA = (225, 29, 72, int(0.58 * 255))
def save_visual_mask_from_binary(mask, out_path):
mask_bool = np.asarray(mask)
if mask_bool.ndim == 3:
mask_bool = np.any(mask_bool > 0, axis=0)
elif mask_bool.dtype != bool:
mask_bool = mask_bool > 0
H, W = mask_bool.shape
out = np.zeros((H, W, 4), dtype=np.uint8)
out[mask_bool] = MASK_OVERLAY_RGBA
Image.fromarray(out, mode="RGBA").save(out_path)
def save_visual_mask(mask_data, out_path):
masks = np.asarray(mask_data)
if masks.ndim == 2:
combined_mask = masks > 0
else:
masks = (masks > 0.5) if masks.dtype != bool else masks.astype(bool)
combined_mask = np.any(masks, axis=0) if masks.size else np.zeros(masks.shape[-2:], dtype=bool)
save_visual_mask_from_binary(combined_mask, out_path)
def mask_image_to_binary(mask_img: Image.Image, size=None) -> Image.Image:
rgba = mask_img.convert("RGBA")
if size is not None and rgba.size != size:
rgba = rgba.resize(size, Image.Resampling.NEAREST)
rgba_arr = np.asarray(rgba)
alpha = rgba_arr[..., 3]
if np.any(alpha < 255):
mask_bool = alpha > 12
else:
gray = np.asarray(rgba.convert("L"))
mask_bool = gray > 127
return Image.fromarray(mask_bool.astype(np.uint8) * 255, mode="L")
def blur_img_with_mask(
original_img: Image.Image,
mask_img: Image.Image,
blur_radius: int = 15,
) -> Image.Image:
original_image = original_img.convert("RGB")
mask_image = mask_img.convert("L")
# Create blurred version of image
blurred_image = original_img.filter(ImageFilter.GaussianBlur(radius=blur_radius))
result = Image.composite(
blurred_image,
original_image,
mask_img,
)
return result
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