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Visualization support for single channel images #288

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Visualization support for single channel images #288

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ef90fdc
added support for single channel images
DmitriyValetov Nov 12, 2024
70f0c33
refactor + test added
DmitriyValetov Nov 16, 2024
bf30196
update to remarks
DmitriyValetov Nov 19, 2024
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10 changes: 9 additions & 1 deletion tests/test_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,9 @@


def test_overlay_mask():
# RGB image
img = Image.fromarray(np.zeros((4, 4, 3)).astype(np.uint8))
mask = Image.fromarray(255 * np.ones((4, 4)).astype(np.uint8))

overlayed = utils.overlay_mask(img, mask, alpha=0.7)

# Check object type
Expand All @@ -16,3 +16,11 @@ def test_overlay_mask():
assert np.all(np.asarray(overlayed)[..., 0] == 0)
assert np.all(np.asarray(overlayed)[..., 1] == 0)
assert np.all(np.asarray(overlayed)[..., 2] == 39)

# grayscale image
img = Image.fromarray(np.zeros((4, 4)).astype(np.uint8))
mask = Image.fromarray(255 * np.ones((4, 4)).astype(np.uint8))
overlayed = utils.overlay_mask(img, mask, alpha=0.7)

# Verify value
assert np.all(np.asarray(overlayed) == 39)
9 changes: 8 additions & 1 deletion torchcam/utils.py
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Expand Up @@ -39,9 +39,16 @@ def overlay_mask(img: Image, mask: Image, colormap: str = "jet", alpha: float =
if not isinstance(alpha, float) or alpha < 0 or alpha >= 1:
raise ValueError("alpha argument is expected to be of type float between 0 and 1")

if len(img.getbands()) not in {1, 3}:
raise ValueError("img argument needs to be a grayscale or RGB image")

cmap = cm.get_cmap(colormap)
# Resize mask and apply colormap
overlay = mask.resize(img.size, resample=Resampling.BICUBIC)
overlay = (255 * cmap(np.asarray(overlay) ** 2)[:, :, :3]).astype(np.uint8)

overlay = (255 * cmap(np.asarray(overlay) ** 2)[:, :, 2 if len(img.getbands()) == 1 else slice(0, 3)]).astype(
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Final question: why taking the last channel, not first or second? Is any of those better for viz in grayscale? Or should we average accross the channel dimension?

np.uint8
)

# Overlay the image with the mask
return fromarray((alpha * np.asarray(img) + (1 - alpha) * cast(np.ndarray, overlay)).astype(np.uint8))
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