Adding a file and feature to delete all detections from database

2026-01-16 13:43:05 +02:00
parent 2c494dac49
commit 20578c1fdf
5 changed files with 191 additions and 28 deletions
--- a/src/utils/create_mask_from_detection.py
+++ b/src/utils/create_mask_from_detection.py
@@ -0,0 +1,103 @@
+import numpy as np
+
+from pathlib import Path
+from skimage.draw import polygon
+from tifffile import TiffFile
+
+from src.database.db_manager import DatabaseManager
+
+
+def read_image(image_path: Path) -> np.ndarray:
+    metadata = {}
+    with TiffFile(image_path) as tif:
+        image = tif.asarray()
+        metadata = tif.imagej_metadata
+    return image, metadata
+
+
+def main():
+
+    polygon_vertices = np.array([[10, 10], [50, 10], [50, 50], [10, 50]])
+    image = np.zeros((100, 100), dtype=np.uint8)
+    rr, cc = polygon(polygon_vertices[:, 0], polygon_vertices[:, 1])
+    image[rr, cc] = 255
+
+
+if __name__ == "__main__":
+
+    db = DatabaseManager()
+    model_name = "c17"
+    model_id = db.get_models(filters={"model_name": model_name})[0]["id"]
+    print(f"Model name {model_name}, id {model_id}")
+    detections = db.get_detections(filters={"model_id": model_id})
+
+    file_stems = set()
+
+    for detection in detections:
+        file_stems.add(detection["image_filename"].split("_")[0])
+
+    print("Files:", file_stems)
+
+    for stem in file_stems:
+        print(stem)
+        detections = db.get_detections(filters={"model_id": model_id, "i.filename": f"LIKE %{stem}%"})
+        annotations = []
+        for detection in detections:
+            source_path = Path(detection["metadata"]["source_path"])
+            image, metadata = read_image(source_path)
+
+            offset = np.array(list(map(int, metadata["tile_section"].split(","))))[::-1]
+            scale = np.array(list(map(int, metadata["patch_size"].split(","))))[::-1]
+            # tile_size = np.array(list(map(int, metadata["tile_size"].split(","))))
+            segmentation = np.array(detection["segmentation_mask"])  # * tile_size
+
+            # print(source_path, image, metadata, segmentation.shape)
+            # print(offset)
+            # print(scale)
+            # print(segmentation)
+
+            # segmentation = (segmentation + offset * tile_size) / (tile_size * scale)
+            segmentation = (segmentation + offset) / scale
+
+            yolo_annotation = f"{detection['metadata']['class_id']} " + " ".join(
+                [f"{x:.6f} {y:.6f}" for x, y in segmentation]
+            )
+            annotations.append(yolo_annotation)
+            # print(segmentation)
+            # print(yolo_annotation)
+
+            # aa
+            print(
+                "  ",
+                detection["model_name"],
+                detection["image_id"],
+                detection["image_filename"],
+                source_path,
+                metadata["label_path"],
+            )
+            # section_i_section_j = detection["image_filename"].split("_")[1].split(".")[0]
+            # print("    ", section_i_section_j)
+
+        label_path = metadata["label_path"]
+        print("  ", label_path)
+        with open(label_path, "w") as f:
+            f.write("\n".join(annotations))
+
+    exit()
+
+    for detection in detections:
+        print(detection["model_name"], detection["image_id"], detection["image_filename"])
+
+    print(detections[0])
+    # polygon_vertices = np.array([[10, 10], [50, 10], [50, 50], [10, 50]])
+
+    # image = np.zeros((100, 100), dtype=np.uint8)
+
+    # rr, cc = polygon(polygon_vertices[:, 0], polygon_vertices[:, 1])
+
+    # image[rr, cc] = 255
+
+    # import matplotlib.pyplot as plt
+
+    # plt.imshow(image, cmap='gray')
+    # plt.show()