Upload 25 files

.gitattributes

@@ -33,3 +33,8 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+images/Dr.Ahmed-Farouk.jpg filter=lfs diff=lfs merge=lfs -text
+images/Eyad.jpg filter=lfs diff=lfs merge=lfs -text
+images/EYAD1.jpg filter=lfs diff=lfs merge=lfs -text
+images/Prof.[[:space:]]Ammar[[:space:]]Mohammed.png filter=lfs diff=lfs merge=lfs -text
+images/Prof.[[:space:]]Ayman[[:space:]]Ezzat.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,76 @@
+import gradio as gr
+import cv2
+import os
+import insightface
+import numpy as np
+from collections import deque
+
+# Load InsightFace model
+model = insightface.app.FaceAnalysis(allowed_modules=['detection', 'recognition'])
+model.prepare(ctx_id=0, det_size=(640, 640))  # CPU: ctx_id=-1
+
+# Normalize function
+def normalize(v):
+    return v / np.linalg.norm(v)
+
+# Cosine similarity function
+def cosine_similarity(a, b):
+    return np.dot(a, b)
+
+# Load known embeddings
+known_embs = []
+names = []
+for fname in os.listdir("images"):
+    if fname.lower().endswith(('.jpg', '.png')):
+        img = cv2.imread(os.path.join("images", fname))
+        faces = model.get(img)
+        if faces:
+            emb = normalize(faces[0].embedding)
+            known_embs.append(emb)
+            names.append(os.path.splitext(fname)[0])
+            print(f"Loaded {fname}")
+        else:
+            print(f"No face in {fname}")
+
+# Recognition function for an uploaded image
+def recognize(image):
+    face_buffers = {}
+    frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
+    faces = model.get(frame)
+    current_buffers = {}
+
+    for face in faces:
+        x1, y1, x2, y2 = face.bbox.astype(int)
+        emb = normalize(face.embedding)
+        face_id = f"{x1}-{y1}-{x2}-{y2}"
+
+        if face_id not in face_buffers:
+            face_buffers[face_id] = deque(maxlen=5)
+
+        face_buffers[face_id].append(emb)
+        current_buffers[face_id] = face_buffers[face_id]
+
+        avg_emb = normalize(np.mean(face_buffers[face_id], axis=0))
+        sims = [cosine_similarity(avg_emb, known) for known in known_embs]
+        max_idx = np.argmax(sims)
+        name = "Unknown"
+        if sims[max_idx] > 0.5:
+            name = names[max_idx]
+
+        # Draw on the frame
+        cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
+        cv2.putText(frame, name, (x1, y1 - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 0), 2)
+
+    return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+
+# Gradio interface
+iface = gr.Interface(
+    fn=recognize,
+    inputs=gr.Image(type="numpy", label="Upload Image"),
+    outputs=gr.Image(type="numpy", label="Recognized Faces"),
+    title="Face Recognition with InsightFace",
+    description="Upload an image, and the system will identify known faces from the 'images/' folder."
+)
+
+if __name__ == "__main__":
+    iface.launch()

requirements.txt

@@ -0,0 +1,4 @@
+insightface
+opencv-python
+gradio
+numpy