Update app.py

app.py

@@ -2,102 +2,107 @@ import os
 import cv2
 import dlib
 import numpy as np
+from PIL import Image
 import gradio as gr
 import git
-from PIL import Image
 
-# === CONFIGURATION ===
+# === Clone le dépôt GitHub contenant Known_faces_clean ===
 REPO_URL = "https://github.com/Moustapha224/Reconnaissance"
-CLONE_DIR = "Reconnaissance"
-KNOWN_FACES_DIR = os.path.join(CLONE_DIR, "Known_faces")
+REPO_DIR = "Reconnaissance"
+KNOWN_FACES_DIR = os.path.join(REPO_DIR, "Known_faces_clean")
 
-# === CLONAGE DU DÉPÔT GITHUB ===
-if not os.path.exists(CLONE_DIR):
+if not os.path.exists(REPO_DIR):
     print(f"🔄 Clonage du dépôt GitHub depuis {REPO_URL} ...")
-    git.Repo.clone_from(REPO_URL, CLONE_DIR)
+    git.Repo.clone_from(REPO_URL, REPO_DIR)
 
-# === DLIB MODELS ===
-PREDICTOR_PATH = "shape_predictor_68_face_landmarks.dat"
-FACE_ENCODER_PATH = "dlib_face_recognition_resnet_model_v1.dat"
+# === Initialisation des outils Dlib ===
 face_detector = dlib.get_frontal_face_detector()
-shape_predictor = dlib.shape_predictor(PREDICTOR_PATH)
-face_encoder = dlib.face_recognition_model_v1(FACE_ENCODER_PATH)
+shape_predictor = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+face_rec_model = dlib.face_recognition_model_v1("dlib_face_recognition_resnet_model_v1.dat")
 
-# === ENCODAGE DES VISAGES CONNUS ===
-known_encodings = []
-known_names = []
+# === Stockage des visages connus ===
+known_face_encodings = []
+known_face_names = []
 
+# === Fonction d'encodage d'une image en vecteur de visage ===
 def get_face_encoding(image):
-    if image is None or len(image.shape) != 3 or image.shape[2] != 3:
-        return None
+    if image is None or len(image.shape) != 3:
+        raise ValueError("Image invalide (None ou pas RGB)")
+    if image.shape[2] == 4:
+        image = cv2.cvtColor(image, cv2.COLOR_RGBA2RGB)
+    elif image.shape[2] != 3:
+        image = cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+
     dets = face_detector(image, 1)
     if len(dets) == 0:
         return None
+
     shape = shape_predictor(image, dets[0])
-    encoding = face_encoder.compute_face_descriptor(image, shape)
-    return np.array(encoding)
+    face_descriptor = face_rec_model.compute_face_descriptor(image, shape)
+    return np.array(face_descriptor)
 
+# === Chargement des visages connus ===
 def load_known_faces():
     print("🔍 Encodage des visages connus...")
-    for person_name in os.listdir(KNOWN_FACES_DIR):
-        person_dir = os.path.join(KNOWN_FACES_DIR, person_name)
-        if not os.path.isdir(person_dir):
+    for person in os.listdir(KNOWN_FACES_DIR):
+        person_path = os.path.join(KNOWN_FACES_DIR, person)
+        if not os.path.isdir(person_path):
             continue
-        for filename in os.listdir(person_dir):
-            file_path = os.path.join(person_dir, filename)
+        for file in os.listdir(person_path):
+            file_path = os.path.join(person_path, file)
             try:
-                # On charge via PIL pour garantir le format
-                with Image.open(file_path) as img:
-                    img = img.convert("RGB")
-                    img_np = np.array(img)
-                encoding = get_face_encoding(img_np)
+                img = Image.open(file_path).convert("RGB")
+                rgb = np.array(img)
+                encoding = get_face_encoding(rgb)
                 if encoding is not None:
-                    known_encodings.append(encoding)
-                    known_names.append(person_name)
+                    known_face_encodings.append(encoding)
+                    known_face_names.append(person)
                 else:
-                    print(f"⚠️ Visage non détecté dans : {file_path}")
+                    print(f"⚠️ Aucun visage détecté dans {file_path}")
             except Exception as e:
-                print(f"❌ Problème avec {file_path}: {e}")
+                print(f"❌ Erreur avec {file_path}: {e}")
 
 load_known_faces()
 
-# === RECONNAISSANCE ===
-def recognize_faces(uploaded_image):
+# === Fonction de reconnaissance faciale ===
+def recognize_faces(image):
+    if image is None:
+        return None
     try:
-        if uploaded_image is None:
-            print("⚠️ Image reçue est None")
-            return None
-        img = cv2.cvtColor(uploaded_image, cv2.COLOR_BGR2RGB)
-        dets = face_detector(img, 1)
-        for det in dets:
-            shape = shape_predictor(img, det)
-            encoding = face_encoder.compute_face_descriptor(img, shape)
-            encoding_np = np.array(encoding)
-
-            distances = np.linalg.norm(known_encodings - encoding_np, axis=1)
-            name = "Inconnu"
-            if len(distances) > 0:
-                min_idx = np.argmin(distances)
-                if distances[min_idx] < 0.6:
-                    name = known_names[min_idx]
-
-            x, y, x1, y1 = det.left(), det.top(), det.right(), det.bottom()
-            cv2.rectangle(uploaded_image, (x, y), (x1, y1), (0, 255, 0), 2)
-            cv2.putText(uploaded_image, name, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)
-
-        return cv2.cvtColor(uploaded_image, cv2.COLOR_BGR2RGB)
-    except Exception as e:
-        print(f"❌ Erreur dans recognize_faces: {e}")
+        img = cv2.cvtColor(np.array(image), cv2.COLOR_RGBA2RGB if image.mode == "RGBA" else cv2.COLOR_RGB2BGR)
+    except:
         return None
 
-# === INTERFACE GRADIO ===
+    dets = face_detector(img, 1)
+    if len(dets) == 0:
+        return image
+
+    for d in dets:
+        shape = shape_predictor(img, d)
+        face_descriptor = face_rec_model.compute_face_descriptor(img, shape)
+        encoding = np.array(face_descriptor)
+
+        distances = np.linalg.norm(known_face_encodings - encoding, axis=1)
+        min_distance = np.min(distances)
+        name = "Inconnu"
+
+        if min_distance < 0.6:
+            name = known_face_names[np.argmin(distances)]
+
+        # Dessiner le rectangle
+        x1, y1, x2, y2 = d.left(), d.top(), d.right(), d.bottom()
+        cv2.rectangle(img, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.putText(img, name, (x1, y2 + 20), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+
+    return Image.fromarray(cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
+
+# === Interface Gradio ===
 iface = gr.Interface(
     fn=recognize_faces,
-    inputs=gr.Image(type="numpy", label="Uploader une image"),
-    outputs=gr.Image(type="numpy", label="Résultat"),
+    inputs=gr.Image(label="Image à reconnaître"),
+    outputs=gr.Image(label="Résultat"),
     title="Reconnaissance Faciale",
-    description="Uploader une photo pour identifier les visages connus."
+    description="Téléversez une image contenant un visage pour l'identifier à partir des visages connus."
 )
 
-if __name__ == "__main__":
-    iface.launch()
+iface.launch()