Delete main.py

main.py

@@ -1,61 +0,0 @@
-import cv2
-from model import EmotionPredictor
-
-cap = cv2.VideoCapture(0)
-predictor = EmotionPredictor()
-
-face_cascade = cv2.CascadeClassifier(
-    cv2.data.haarcascades + "haarcascade_frontalface_default.xml"
-)
-
-if face_cascade.empty():
-    raise RuntimeError("Failed to load Haar Cascade")
-
-FRAME_SKIP = 2
-frame_count = 0
-current_faces = []
-
-
-while True:
-    ret, frame = cap.read()
-    if not ret:
-        break
-
-    frame_count += 1
-
-    if frame_count % FRAME_SKIP == 0:
-        gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
-        detected = face_cascade.detectMultiScale(
-            gray, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30)
-        )
-        current_faces = []
-        if len(detected) > 0:
-            x, y, w, h = max(detected, key=lambda r: r[2]*r[3])
-            
-            y1, y2 = max(0, y), min(frame.shape[0], y + h)
-            x1, x2 = max(0, x), min(frame.shape[1], x + w)
-            
-            if y2 > y1 and x2 > x1:
-                face_rgb = cv2.cvtColor(frame[y1:y2, x1:x2], cv2.COLOR_BGR2RGB)
-                label = predictor.predict(face_rgb)
-                current_faces.append((x, y, w, h, label))
-
-    for (x, y, w, h, label) in current_faces:
-        cv2.rectangle(frame, (x, y), (x+w, y+h), (255, 0, 0), 2)
-        cv2.putText(
-            frame, label, (x, y - 10),
-            cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 255, 255), 2
-        )
-
-    cv2.imshow("Emotion Detection", frame)
-
-    if cv2.waitKey(1) & 0xFF == ord("q"):
-        break
-    if cv2.getWindowProperty("Emotion Detection", cv2.WND_PROP_VISIBLE) < 1:
-        break
-
-cap.release()
-cv2.destroyAllWindows()
-
-
-