Add plot of ROI contours w/o IDs

README.md

@@ -12,7 +12,7 @@ of the _registered_ (or, in the case of multi-plane imaging, _decrosstalked_) mo
 ## Output
 
 The main output is the `extraction.h5` file.   
-For QC, the figure `detected_ROIs.png` displays contours of the detected ROIs over a selection of summary images.
+For QC, the figures `detected_ROIs.png` and `detected_ROIs_withIDs.png` display contours of the detected ROIs over a selection of summary images.
 If the capsule is run with the `--contour_video` argument, a 10x sped-up video is also created, showing ROI contours overlaid on the raw data, along with ROI activity and the remainder.
 <br>
 The `extraction.h5` file contains three groups: `traces`, `rois`, and `cellpose`, with the latter only present if Cellpose is used for detection (--anatomical_only ≠ 0).

code/extraction.py

@@ -843,22 +843,23 @@ def contour_video(
         corr_img = max_corr_image(f["data"])
     # plot
     x_size = 8.5 * max(dims[1] / dims[0], 0.4)
-    plt.figure(figsize=(x_size, 3))
+    fix, ax = plt.subplots(1, 3, figsize=(x_size, 3))
     for i, img in enumerate((ops["meanImg"], ops["max_proj"], corr_img)):
-        plt.subplot(1, 3, 1 + i)
         vmin, vmax = np.nanpercentile(img, (1, 99))
-        plt.imshow(img, interpolation=None, cmap="gray", vmin=vmin, vmax=vmax)
+        ax[i].imshow(img, interpolation=None, cmap="gray", vmin=vmin, vmax=vmax)
         for c in coordinates:
-            plt.plot(*c["coordinates"].T, c="orange")
-        for k in range(rois.shape[0]):
-            plt.text(*cm[k], str(k), color="orange")
+            ax[i].plot(*c["coordinates"].T, c="orange")
         plt.axis("off")
         plt.title(
             ("mean image", "max image", "correlation image")[i],
             fontsize=min(12, 2.4 + 2 * x_size),
         )
     plt.tight_layout(pad=0.1)
     plt.savefig(output_dir / "detected_ROIs.png", bbox_inches="tight", pad_inches=0.02)
+    for i in (0, 1, 2):
+        for k in range(rois.shape[0]):
+            ax[i].text(*cm[k], str(k), color="orange")
+    plt.savefig(output_dir / "detected_ROIs_withIDs.png", bbox_inches="tight", pad_inches=0.02)
 
     if args.contour_video:
         with h5py.File(str(motion_corrected_fn), "r") as f: