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feat(plot): add share_axes to show_segmentation for synced viewing #1405

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feat(plot): add share_axes to show_segmentation for synced viewing #1405

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52 changes: 29 additions & 23 deletions cellpose/plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -44,7 +44,7 @@ def dx_to_circ(dP):
return rgb


def show_segmentation(fig, img, maski, flowi, channels=[0, 0], file_name=None):
def show_segmentation(fig, img, maski, flowi, channels=[0, 0], file_name=None, seg_norm=False, share_axes=False):
"""Plot segmentation results (like on website).

Can save each panel of figure with file_name option. Use channels option if
Expand All @@ -58,47 +58,53 @@ def show_segmentation(fig, img, maski, flowi, channels=[0, 0], file_name=None):
channels (list of int, optional): Channels used to run Cellpose, no need to use if image is RGB. Defaults to [0, 0].
file_name (str, optional): File name of image. If file_name is not None, figure panels are saved. Defaults to None.
seg_norm (bool, optional): Improve cell visibility under labels. Defaults to False.
share_axes (bool, optional): Share x and y axes between subplots for synchronized zooming. Defaults to False.
"""
if not MATPLOTLIB_ENABLED:
raise ImportError(
"matplotlib not installed, install with 'pip install matplotlib'")
ax = fig.add_subplot(1, 4, 1)

# 1. Process initial image
img0 = img.copy()

if img0.shape[0] < 4:
img0 = np.transpose(img0, (1, 2, 0))
if img0.shape[-1] < 3 or img0.ndim < 3:
img0 = image_to_rgb(img0, channels=channels)
else:
if img0.max() <= 50.0:
img0 = np.uint8(np.clip(img0, 0, 1) * 255)
ax.imshow(img0)
ax.set_title("original image")
ax.axis("off")

# 2. Prepare plot data
outlines = utils.masks_to_outlines(maski)

overlay = mask_overlay(img0, maski)

ax = fig.add_subplot(1, 4, 2)

outX, outY = np.nonzero(outlines)
imgout = img0.copy()
imgout[outX, outY] = np.array([255, 0, 0]) # pure red

ax.imshow(imgout)
ax.set_title("predicted outlines")
ax.axis("off")

ax = fig.add_subplot(1, 4, 3)
ax.imshow(overlay)
ax.set_title("predicted masks")
ax.axis("off")
imgout[outX, outY] = np.array([255, 0, 0])

# List of (data, title) for the 4 subplots
plot_data = [
(img0, "original image"),
(imgout, "predicted outlines"),
(overlay, "predicted masks"),
(flowi, "predicted cell pose")
]

ax = fig.add_subplot(1, 4, 4)
ax.imshow(flowi)
ax.set_title("predicted cell pose")
ax.axis("off")
# 3. Create subplots in a loop
axes = []
for i, (data, title) in enumerate(plot_data):
# Determine sharing: only for subplots index 1, 2, 3 and if share_axes is True
if i > 0 and share_axes:
ax = fig.add_subplot(1, 4, i + 1, sharex=axes[0], sharey=axes[0])
else:
ax = fig.add_subplot(1, 4, i + 1)

ax.imshow(data)
ax.set_title(title)
ax.axis("off")
axes.append(ax)

# 4. Handle file saving
if file_name is not None:
save_path = os.path.splitext(file_name)[0]
io.imsave(save_path + "_overlay.jpg", overlay)
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