Removed qc from the directory cleaning

README.md

@@ -39,13 +39,74 @@ both be downloaded; the uploaded source image remains unchanged.
 
 For batch processing, open the **Image directory** tab and select a folder.
 The app processes all uploaded images, including `.tif` and `.tiff` files, and
-returns a ZIP archive containing `masks/` and `qc_cleaned/` folders.
+returns a ZIP archive containing only the cleaned images.
 
 Project layout:
 
 - `main.py` — primary image-cleaning script
 - `cleaning.py` — reusable cleaning operations
+- `ihc_quantification_simplified.py` — per-spheroid DAB quantification
+- `ihc_qc.py` — reusable segmentation, debris, and DAB QC drawing
 - `app.py` — optional Gradio interface
 - `data/` — source and example images
 - `out/` — generated cleaned images and masks
 - `test.ipynb` — image-cleaning experiments
+
+## DAB quantification
+
+`ihc_quantification_simplified.py` segments individual spheroids, excludes the
+debris mask, separates H-DAB stains from the original image values, and writes
+one CSV row per spheroid. Image metadata such as treatment, image number, and
+an optional image note is extracted from the filename.
+
+```bash
+uv run python ihc_quantification_simplified.py data/dab_quantification_test
+```
+
+The input can be one TIFF image or a directory. Directories are searched
+recursively. Results from all discovered images are combined in
+`out/quantification/ihc_quantification.csv` by default. Each spheroid is one
+row, so the table can be filtered or grouped by treatment in Excel or pandas.
+One two-panel QC image per source image is saved in
+`out/quantification/qc/`, showing the segmented spheroids, excluded debris,
+and measured DAB signal. The DAB heatmap uses the same fixed `0-0.3` scale for
+every image so its colors can be compared directly across the dataset.
+
+Use `--output` to choose another CSV location:
+
+```bash
+uv run python ihc_quantification_simplified.py data/treatment_images \
+  --output out/my_results.csv
+```
+
+With a custom output path, QC images are placed in a `qc/` directory beside
+the CSV.
+
+Experiment-sensitive boundary and watershed settings can be changed without
+editing the script:
+
+```bash
+uv run python ihc_quantification_simplified.py data/treatment_images \
+  --max-boundary-contact-ratio 0.3 \
+  --watershed-marker-core-fraction 0.55 \
+  --watershed-minimum-relative-region-area 0.3
+```
+
+A larger boundary-contact ratio tolerates more image-edge contact. Lowering
+the minimum relative watershed-region area permits more uneven splits, such as
+a complete spheroid touching a partially visible spheroid. The marker-core
+fraction controls which distance-transform cores seed the watershed.
+
+The QC image can also be created directly from a notebook:
+
+```python
+from ihc_qc import create_qc_image
+
+qc = create_qc_image(preview, labels, debris_mask, dab)
+```
+
+Pass `dab_vmax` to use another fixed upper limit for a particular analysis:
+
+```python
+qc = create_qc_image(preview, labels, debris_mask, dab, dab_vmax=1.0)
+```

app.py

@@ -8,6 +8,7 @@ import gradio as gr
 import numpy as np
 
 from cleaning import (
+    clean_image,
     clean_image_and_mask,
     cleaned_image_name,
     read_image_rgb,
@@ -99,36 +100,26 @@ def clean_directory(image_paths: list[str] | None) -> tuple[str, str]:
 
     batch_dir = Path(_BATCH_OUTPUTS.name) / uuid4().hex
     cleaned_dir = batch_dir / "cleaned"
-    masks_dir = batch_dir / "masks"
     cleaned_dir.mkdir(parents=True)
-    masks_dir.mkdir(parents=True)
 
     used_names: set[str] = set()
 
     for image_path_string in image_paths:
         image_path = Path(image_path_string)
-        cleaned_rgb, debris_mask = clean_image_and_mask(
-            read_image_rgb(image_path)
-        )
+        cleaned_rgb = clean_image(read_image_rgb(image_path))
 
         output_name = cleaned_image_name(image_path, used_names)
         write_image_rgb(cleaned_dir / output_name, cleaned_rgb)
-        mask_name = f"{Path(output_name).stem}_debris_mask.png"
-        mask_path = masks_dir / mask_name
-        if not cv2.imwrite(str(mask_path), debris_mask):
-            raise OSError(f"Could not write debris mask: {mask_path}")
 
     archive_path = batch_dir / "cleaned_images.zip"
     with zipfile.ZipFile(archive_path, "w", zipfile.ZIP_DEFLATED) as archive:
         for output_path in sorted(cleaned_dir.iterdir()):
-            archive.write(output_path, arcname=f"qc_cleaned/{output_path.name}")
-        for output_path in sorted(masks_dir.iterdir()):
-            archive.write(output_path, arcname=f"masks/{output_path.name}")
+            archive.write(output_path, arcname=output_path.name)
 
     count = len(used_names)
     return (
         str(archive_path),
-        f"Created QC previews and debris masks for {count} "
+        f"Created cleaned images for {count} "
         f"image{'s' if count != 1 else ''}.",
     )
 
@@ -236,7 +227,7 @@ def build_app() -> gr.Blocks:
             )
             batch_status = gr.Textbox(label="Status", interactive=False)
             batch_download = gr.File(
-                label="QC images and debris masks",
+                label="Cleaned images",
                 interactive=False,
             )