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on
Zero
Running
on
Zero
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import numpy as np
import matplotlib.pyplot as plt
import cv2
#Completeöy vibecoded
def visualize_prediction(
image: np.ndarray,
anomaly_map: np.ndarray,
image_score: float,
threshold_percentile: float = 95.0,
title: str | None = None,
) -> None:
"""
Show:
- original image
- heatmap overlay
- binary mask overlay (thresholded)
"""
# Normalize anomaly map to [0, 1] for visualization
amap = anomaly_map.astype(np.float32)
amap -= amap.min()
if amap.max() > 0:
amap /= amap.max()
thresh = np.percentile(amap, threshold_percentile)
binary = amap >= thresh
fig, axes = plt.subplots(1, 3, figsize=(14, 4))
axes[0].imshow(image)
axes[0].set_title("Input image")
axes[0].axis("off")
axes[1].imshow(image)
im = axes[1].imshow(amap, cmap="jet", alpha=0.5)
axes[1].set_title("Anomaly heatmap")
axes[1].axis("off")
fig.colorbar(im, ax=axes[1], fraction=0.046, pad=0.04)
axes[2].imshow(image)
axes[2].imshow(binary, cmap="gray", alpha=0.5)
axes[2].set_title(f"Thresholded (>{threshold_percentile:.0f}%)")
axes[2].axis("off")
if title is None:
title = f"Image anomaly score: {image_score:.3f}"
fig.suptitle(title)
plt.tight_layout()
plt.show()
def visualize_segmentation(
image: np.ndarray,
full_mask: np.ndarray,
grid_size: tuple[int, int] | None = None,
title: str | None = None,
) -> None:
"""
Visualize SAM2 segmentation.
Args:
image: (H, W, 3) RGB uint8
full_mask: (H, W) bool or 0/1 array from SAM2
grid_size: optional (H_patches, W_patches) to also show patch-level mask
title: optional title string
"""
img = image
mask = full_mask.astype(bool)
H, W = mask.shape
# Prepare figure layout
n_cols = 3 if grid_size is None else 4
fig, axes = plt.subplots(1, n_cols, figsize=(4 * n_cols, 4))
# 1) input image
axes[0].imshow(img)
axes[0].set_title("Input image")
axes[0].axis("off")
# 2) raw binary mask
axes[1].imshow(mask, cmap="gray")
axes[1].set_title("SAM2 mask (full-res)")
axes[1].axis("off")
# 3) overlay mask on image
axes[2].imshow(img)
axes[2].imshow(mask, cmap="Reds", alpha=0.4)
axes[2].set_title("Mask overlay")
axes[2].axis("off")
# 4) optional patch-level mask (after downsampling)
if grid_size is not None:
gh, gw = grid_size
# downsample full mask to patch grid and back up to image size
patch_mask_small = cv2.resize(
mask.astype(np.uint8), (gw, gh), interpolation=cv2.INTER_NEAREST
).astype(bool)
patch_mask_full = cv2.resize(
patch_mask_small.astype(np.uint8),
(W, H),
interpolation=cv2.INTER_NEAREST,
).astype(bool)
axes[3].imshow(img)
axes[3].imshow(patch_mask_full, cmap="Blues", alpha=0.4)
axes[3].set_title("Patch-level mask (after downsample)")
axes[3].axis("off")
if title is not None:
fig.suptitle(title)
plt.tight_layout()
plt.show() |