Fixes/multi anonymization

.vscode/launch.json

@@ -13,5 +13,14 @@
             "console": "integratedTerminal",
             "justMyCode": false,
         },
+        {
+            "name": "Multi anonymization",
+            "type": "debugpy",
+            "request": "launch",
+            "program": "${workspaceFolder}/model_face/main_multi_anonymize.py",
+            "cwd": "${workspaceFolder}",
+            "console": "integratedTerminal",
+            "justMyCode": false,
+        },
     ],
 }

model_face/detector/yolov8_face_detector.py

@@ -115,6 +115,8 @@ def detect(self, srcimg: np.ndarray) -> sv.Detections:
 
         # Perform inference on the image
         det_xywh, det_conf, det_classid, landmarks = self.post_process(outputs, scale_h, scale_w, padh, padw)
+        if len(det_xywh) == 0:
+            return sv.Detections.empty()
 
         # Padding : Increase width/height by +padding in px
         det_xywh[:, 2:] += self.padding
@@ -180,12 +182,17 @@ def post_process(self, preds, scale_h, scale_w, padh, padw):
         class_ids = class_ids[mask]
         landmarks = landmarks[mask]
 
+        # Check : No detections
+        if len(bboxes_wh) == 0:
+            return np.array([]), np.array([]), np.array([]), np.array([])
+
         indices = cv2.dnn.NMSBoxes(
             bboxes_wh.tolist(),
             confidences.tolist(),
             self.conf_threshold,
             self.iou_threshold,
         ).flatten()  ## type: ignore
+
         if len(indices) > 0:
             mlvl_bboxes = bboxes_wh[indices]
             confidences = confidences[indices]

model_face/main.py → model_face/main_anonymize.py

@@ -18,34 +18,6 @@ def configure_logging() -> None:
     )
 
 
-def main_detect() -> None:
-    parser = argparse.ArgumentParser()
-    parser.add_argument("--imgpath", type=str, default="images/1.jpg", help="image path")
-    parser.add_argument(
-        "--modelpath",
-        type=str,
-        default="zoo/yolov8n-face.onnx",
-        help="onnx filepath",
-    )
-    parser.add_argument("--confThreshold", default=0.20, type=float, help="class confidence")
-    parser.add_argument("--nmsThreshold", default=0.40, type=float, help="nms iou thresh")
-    args = parser.parse_args()
-
-    # Initialize YOLOv8_face object detector
-    source = cv2.imread(args.imgpath)
-
-    # Detect Objects
-    detector = YOLOv8FaceDetection(args.modelpath, conf_thres=args.confThreshold, iou_thres=args.nmsThreshold)
-    detections = detector.detect(source)
-
-    # Draw detections
-    annotated = source.copy()
-    annotator = sv.BoxAnnotator()
-    scene = annotator.annotate(scene=annotated, detections=detections)
-    cv2.imshow("YOLOv8 Face Detection", scene)
-    cv2.waitKey(0)
-
-
 def main_anonymize() -> None:
     """Main function to run the detection."""
     # Configure logging

model_face/main_detection.py

@@ -0,0 +1,49 @@
+import argparse
+import logging
+
+import cv2
+import supervision as sv
+
+from model_face.detector.yolov8_face_detector import YOLOv8FaceDetection
+
+logger = logging.getLogger(__name__)
+
+
+def configure_logging() -> None:
+    """Configure logging for the application."""
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
+
+
+def main_detect() -> None:
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--imgpath", type=str, default="images/1.jpg", help="image path")
+    parser.add_argument(
+        "--modelpath",
+        type=str,
+        default="zoo/yolov8n-face.onnx",
+        help="onnx filepath",
+    )
+    parser.add_argument("--confThreshold", default=0.20, type=float, help="class confidence")
+    parser.add_argument("--nmsThreshold", default=0.40, type=float, help="nms iou thresh")
+    args = parser.parse_args()
+
+    # Initialize YOLOv8_face object detector
+    source = cv2.imread(args.imgpath)
+
+    # Detect Objects
+    detector = YOLOv8FaceDetection(args.modelpath, conf_thres=args.confThreshold, iou_thres=args.nmsThreshold)
+    detections = detector.detect(source)
+
+    # Draw detections
+    annotated = source.copy()
+    annotator = sv.BoxAnnotator()
+    scene = annotator.annotate(scene=annotated, detections=detections)
+    cv2.imshow("YOLOv8 Face Detection", scene)
+    cv2.waitKey(0)
+
+
+if __name__ == "__main__":
+    main_detect()

model_face/main_multi_anonymize.py

@@ -0,0 +1,128 @@
+import argparse
+import logging
+import os
+from typing import Any
+
+import cv2
+import supervision as sv
+from tqdm import tqdm  # type: ignore
+from yaya_tools.helpers.dataset import load_directory_images_annotatations  # type: ignore
+
+from model_face.detector.yolov8_face_detector import YOLOv8FaceDetection
+from model_face.helpers.transformations import blur_box  # type: ignore
+
+logger = logging.getLogger(__name__)
+
+
+def anonymize_file(detector: YOLOv8FaceDetection, image_name: str, source_directory: str) -> tuple[str, bool]:
+    """Anonymize an image by detecting faces and applying a blur to the detected regions."""
+    try:
+        source_path = os.path.join(source_directory, image_name)
+        image = cv2.imread(source_path)
+        if image is None:
+            logger.error(f"Could not read image {image_name}")
+            return image_name, False
+
+        detections = detector.detect(image)
+
+        # Logging : How many faces detected
+        if detections == sv.Detections.empty():
+            logger.info("No faces on %s image.", image_name)
+        else:
+            logger.info(
+                "Detected %u faces inside %s image. Average conf %2.2f.",
+                len(detections),
+                image_name,
+                detections.confidence.mean(),  # type: ignore
+            )
+
+        # Anonymize
+        anonymized_im = image
+        for xyxy in detections.xyxy:
+            anonymized_im = blur_box(image=anonymized_im, box=xyxy)
+
+        #  Save : Only if inplace is set and found faces
+        if detections != sv.Detections.empty():
+            cv2.imwrite(source_path, anonymized_im)
+            logger.info(f"Saved anonymized image to {source_path}")
+
+        return image_name, True
+
+    except Exception as e:
+        logger.error(f"Error anonymizing image {image_name}: {e}")
+        return image_name, False
+
+
+def multiprocess_anonymize(
+    detector: YOLOv8FaceDetection,
+    source_directory: str,
+    images_names: list[str],
+    pool_size: int = 5,
+) -> tuple[list[str], list[str]]:
+    """
+    Anonymizes images using a single loop instead of multiprocessing,
+    since the detector object cannot be pickled.
+    """
+    sucess_files: list[str] = []
+    failed_files: list[str] = []
+    for image_name in tqdm(images_names, desc="Anonymizing images"):
+        result = anonymize_file(detector, image_name, source_directory)
+        if result[1]:
+            sucess_files.append(image_name)
+        else:
+            failed_files.append(image_name)
+
+    return sucess_files, failed_files
+
+
+def configure_logging() -> None:
+    """Configure logging for the application."""
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(name)s - %(levelname)s - %(message)s",
+    )
+
+
+def main_anonymize() -> None:
+    """Main function to run the detection."""
+    # Configure logging
+    configure_logging()
+
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--images", type=str, default="images/", help="Directory of images to anonymize in place")
+    parser.add_argument(
+        "--modelpath",
+        type=str,
+        default="zoo/yolov8n-face.onnx",
+        help="onnx filepath",
+    )
+    parser.add_argument("--confThreshold", default=0.10, type=float, help="class confidence")
+    parser.add_argument("--nmsThreshold", default=0.40, type=float, help="nms iou thresh")
+    parser.add_argument("--pixelate", action="store_true", help="pixelate the face instead of blurring")
+    parser.add_argument("--padding", default=5, type=int, help="Bounding box padding size in px")
+    args = parser.parse_args()
+
+    # Initialize YOLOv8_face object detector
+    images_annotated: dict[str, Any] = load_directory_images_annotatations(args.images)
+
+    # Detector : Create
+    detector = YOLOv8FaceDetection(
+        args.modelpath,
+        conf_thres=args.confThreshold,
+        iou_thres=args.nmsThreshold,
+        padding=args.padding,
+    )
+
+    # Multiprocess : Anonymize
+    logger.info("Anonymizing images...")
+    sucess_files, failed_files = multiprocess_anonymize(
+        detector=detector,
+        source_directory=args.images,
+        images_names=list(images_annotated.keys()),
+        pool_size=5,
+    )
+    logger.info("Anonymization completed.")
+
+
+if __name__ == "__main__":
+    main_anonymize()

pyproject.toml

@@ -10,6 +10,7 @@ dependencies = [
   "dotenv>=0.9.9",
   "opencv-python>=4.11.0.86",
   "supervision>=0.25.1",
+  "yaya-tools",
 ]
 
 
@@ -91,7 +92,11 @@ markers = [
   "gpu: marks tests that require GPU",
 ]
 
+[tool.uv.sources]
+yaya-tools = { url = "https://github.com/AISP-PL/yaya-tools/releases/download/v1.2.12/yaya_tools-1.1.0-py3-none-any.whl" }
+
 # Scripts
 [project.scripts]
-face-detect = "model_face.main:main_detect"
-face-anonymize = "model_face.main:main_anonymize"
+face-detect = "model_face.main_detection:main_detect"
+face-anonymize = "model_face.main_anonymize:main_anonymize"
+face-multi-anonymize = "model_face.main_multi_anonymize:main_anonymize"