[Feat] Implemented the search feature by uploading a image -2nd PR

.github/ISSUE_TEMPLATE/bug.yml

@@ -36,8 +36,3 @@ body:
           required: true
         - label: "Do Your changes passes all tests?"
           required: false
-
-
-
-
-

.github/ISSUE_TEMPLATE/feature.yml

@@ -36,4 +36,3 @@ body:
           required: true
         - label: "Does it contain any style related issues?"
           required: false
-

.github/release-drafter-config.yml

@@ -1,24 +1,24 @@
 name-template: PictoPy v$RESOLVED_VERSION
-tag-template: 'v$RESOLVED_VERSION'
+tag-template: "v$RESOLVED_VERSION"
 
 categories:
-  - title: 'Features:'
+  - title: "Features:"
     labels:
-      - 'UI'
-      - 'enhancement'
+      - "UI"
+      - "enhancement"
 
-  - title: 'Bug Fixes:'
+  - title: "Bug Fixes:"
     labels:
-      - 'bug'
+      - "bug"
 
-  - title: 'Documentation:'
+  - title: "Documentation:"
     labels:
-      - 'documentation'
+      - "documentation"
 
-  - title: 'Others:'
+  - title: "Others:"
     labels: []
 
-change-template: '- $TITLE (#$NUMBER) by @$AUTHOR'
+change-template: "- $TITLE (#$NUMBER) by @$AUTHOR"
 
 template: |
   # What's Changed
@@ -27,4 +27,4 @@ template: |
 
   ## Special thanks to all our contributors:
 
-  $CONTRIBUTORS
+  $CONTRIBUTORS

.gitignore

@@ -1,5 +1,6 @@
 # Logs
 logs
+.env
 *.log
 npm-debug.log*
 yarn-debug.log*
@@ -29,4 +30,4 @@ videos_cache.txt
 images_cache.txt
 videos_cache.txt
 venv/
-frontend/dist
+frontend/dist

backend/app/config/settings.py

@@ -4,6 +4,7 @@
 # Microservice URLs
 SYNC_MICROSERVICE_URL = "http://localhost:8001/api/v1"
 
+CONFIDENCE_PERCENT = 0.6
 # Object Detection Models:
 SMALL_OBJ_DETECTION_MODEL = f"{MODEL_EXPORTS_PATH}/YOLOv11_Small.onnx"
 NANO_OBJ_DETECTION_MODEL = f"{MODEL_EXPORTS_PATH}/YOLOv11_Nano.onnx"

backend/app/database/faces.py

@@ -133,6 +133,75 @@ def db_insert_face_embeddings_by_image_id(
             image_id, embeddings, confidence, bbox, cluster_id
         )
 
+def get_all_face_embeddings():
+    conn = sqlite3.connect(DATABASE_PATH)
+    cursor = conn.cursor()
+
+    try:
+        cursor.execute("""
+            SELECT
+                f.embeddings,
+                f.bbox,
+                i.id, 
+                i.path, 
+                i.folder_id, 
+                i.thumbnailPath, 
+                i.metadata, 
+                i.isTagged,
+                m.name as tag_name
+            FROM faces f
+            JOIN images i ON f.image_id=i.id
+            LEFT JOIN image_classes ic ON i.id = ic.image_id
+            LEFT JOIN mappings m ON ic.class_id = m.class_id
+        """)
+        results = cursor.fetchall()
+
+        images_dict = {}
+        for (
+            embeddings,
+            bbox,
+            image_id,
+            path,
+            folder_id,
+            thumbnail_path,
+            metadata,
+            is_tagged,
+            tag_name,
+        ) in results:
+            if image_id not in images_dict:
+                try:
+                    embeddings_json = json.loads(embeddings)
+                    bbox_json = json.loads(bbox)
+                except json.JSONDecodeError:
+                    continue;
+                images_dict[image_id] = {
+                    "embeddings": embeddings_json,
+                    "bbox": bbox_json,
+                    "id": image_id,
+                    "path": path,
+                    "folder_id": folder_id,
+                    "thumbnailPath": thumbnail_path,
+                    "metadata": metadata,
+                    "isTagged": bool(is_tagged),
+                    "tags": [],
+                }
+
+            # Add tag if it exists
+            if tag_name:
+                images_dict[image_id]["tags"].append(tag_name)
+
+        # Convert to list and set tags to None if empty
+        images = []
+        for image_data in images_dict.values():
+            if not image_data["tags"]:
+                image_data["tags"] = None
+            images.append(image_data)
+
+        # Sort by path
+        images.sort(key=lambda x: x["path"])
+        return images
+    finally:
+        conn.close()
 
 def db_get_faces_unassigned_clusters() -> List[Dict[str, Union[FaceId, FaceEmbedding]]]:
     """

backend/app/models/FaceDetector.py

@@ -19,7 +19,7 @@ def __init__(self):
         self._initialized = True
         print("FaceDetector initialized with YOLO and FaceNet models.")
 
-    def detect_faces(self, image_id: int, image_path: str):
+    def detect_faces(self, image_id: str, image_path: str, forSearch: bool = False):
         img = cv2.imread(image_path)
         if img is None:
             print(f"Failed to load image: {image_path}")
@@ -51,7 +51,7 @@ def detect_faces(self, image_id: int, image_path: str):
                 embedding = self.facenet.get_embedding(processed_face)
                 embeddings.append(embedding)
 
-        if embeddings:
+        if (not forSearch and embeddings):
             db_insert_face_embeddings_by_image_id(
                 image_id, embeddings, confidence=confidences, bbox=bboxes
             )

backend/app/routes/face_clusters.py

@@ -1,10 +1,19 @@
+import logging
+import uuid
+import os
+from typing import Optional, List
+from pydantic import BaseModel
+from app.config.settings import CONFIDENCE_PERCENT, DEFAULT_FACENET_MODEL
 from fastapi import APIRouter, HTTPException, status
 from app.database.face_clusters import (
     db_get_cluster_by_id,
     db_update_cluster,
     db_get_all_clusters_with_face_counts,
     db_get_images_by_cluster_id,  # Add this import
 )
+from app.database.faces import get_all_face_embeddings
+from app.models.FaceDetector import FaceDetector
+from app.models.FaceNet import FaceNet
 from app.schemas.face_clusters import (
     RenameClusterRequest,
     RenameClusterResponse,
@@ -17,8 +26,35 @@
     GetClusterImagesData,
     ImageInCluster,
 )
+from app.schemas.images import AddSingleImageRequest
+from app.utils.FaceNet import FaceNet_util_cosine_similarity
 
 
+class BoundingBox(BaseModel):
+    x: float
+    y: float
+    width: float
+    height: float
+
+
+class ImageData(BaseModel):
+    id: str
+    path: str
+    folder_id: str
+    thumbnailPath: str
+    metadata: str
+    isTagged: bool
+    tags: Optional[List[str]] = None
+    bboxes: BoundingBox
+
+
+class GetAllImagesResponse(BaseModel):
+    success: bool
+    message: str
+    data: List[ImageData]
+
+
+logger = logging.getLogger(__name__)
 router = APIRouter()
 
 
@@ -194,3 +230,52 @@ def get_cluster_images(cluster_id: str):
                 message=f"Unable to retrieve images for cluster: {str(e)}",
             ).model_dump(),
         )
+
+
+@router.post(
+    "/face-search",
+    responses={code: {"model": ErrorResponse} for code in [400, 500]},
+)
+def face_tagging(payload: AddSingleImageRequest):
+    image_path = payload.path
+    if not os.path.isfile(image_path):
+        raise HTTPException(
+            status_code=status.HTTP_400_BAD_REQUEST,
+            detail=ErrorResponse(
+                success=False,
+                error="Invalid file path",
+                message="The provided path is not a valid file",
+            ).model_dump(),
+        )
+
+    fd = FaceDetector()
+    fn = FaceNet(DEFAULT_FACENET_MODEL)
+    try:
+        matches = []
+        image_id = str(uuid.uuid4())
+        result = fd.detect_faces(image_id, image_path, forSearch=True)
+        if not result or result["num_faces"] == 0:
+            return GetAllImagesResponse(success=True, message=f"Successfully retrieved {len(matches)} images", data=[])
+
+        process_face = result["processed_faces"][0]
+        new_embedding = fn.get_embedding(process_face)
+
+        images = get_all_face_embeddings()
+        if len(images) == 0:
+            return GetAllImagesResponse(success=True, message=f"Successfully retrieved {len(matches)} images", data=[])
+        else:
+            for image in images:
+                max_similarity = 0
+                similarity = FaceNet_util_cosine_similarity(new_embedding, image["embeddings"])
+                max_similarity = max(max_similarity, similarity)
+                if max_similarity >= CONFIDENCE_PERCENT:
+                    matches.append(ImageData(id=image["id"], path=image["path"], folder_id=image["folder_id"], thumbnailPath=image["thumbnailPath"], metadata=image["metadata"], isTagged=image["isTagged"], tags=image["tags"], bboxes=image["bbox"]))
+
+            return GetAllImagesResponse(
+                success=True,
+                message=f"Successfully retrieved {len(matches)} images",
+                data=matches,
+            )
+    finally:
+        fd.close()
+        fn.close()