Ashu1

computer_vision/emotion_detection/emotion.py

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+import cv2
+import numpy as np
+import dlib
+from deepface import DeepFace
+from PIL import Image
+from collections import deque
+
+# Load Dlib's face detector (more accurate than OpenCV Haar Cascade)
+detector = dlib.get_frontal_face_detector()
+
+# Load the webcam
+cap = cv2.VideoCapture(0)
+
+# Maintain a buffer of last N predictions for smoothing
+emotion_queue = deque(maxlen=5)
+
+# Load emoji images (ensure these exist in the same directory)
+emoji_dict = {
+    "happy": "happy_emoji.jpeg",
+    "sad": "sad_emoji.webp",
+    "angry": "angry_emoji.jpg",
+    "surprise": "surprise_emoji.png",
+    "neutral": "neutral_emoji.webp",
+}
+
+while True:
+    ret, frame = cap.read()
+    if not ret:
+        break
+
+    # Convert frame to grayscale for better face detection
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Detect faces using Dlib
+    faces = detector(gray)
+
+    if faces:
+        # Use DeepFace to analyze the face in the frame
+        try:
+            analysis = DeepFace.analyze(
+                frame,
+                actions=["emotion"],
+                enforce_detection=False,  # ✅ FIXED: Removed 'model_name'
+            )
+            detected_emotion = analysis[0]["dominant_emotion"]
+
+            # Add the latest emotion to the queue
+            emotion_queue.append(detected_emotion)
+
+            # Get the most common emotion from the last few frames (smoothing)
+            if len(emotion_queue) > 2:
+                detected_emotion = max(set(emotion_queue), key=emotion_queue.count)
+
+            # Load corresponding emoji
+            emoji_path = emoji_dict.get(detected_emotion, "neutral_emoji.png")
+            emoji = Image.open(emoji_path).resize((100, 100))
+
+            # Convert OpenCV frame to PIL image for overlay
+            frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+            frame_pil.paste(emoji, (50, 50), emoji)
+
+            # Convert back to OpenCV format
+            frame = cv2.cvtColor(np.array(frame_pil), cv2.COLOR_RGB2BGR)
+
+            # Display detected emotion text
+            cv2.putText(
+                frame,
+                detected_emotion.capitalize(),
+                (50, 180),
+                cv2.FONT_HERSHEY_SIMPLEX,
+                1,
+                (0, 255, 0),
+                2,
+            )
+
+        except Exception as e:
+            print("Error:", e)
+
+    # Show the processed frame
+    cv2.imshow("Live Emoji Detection", frame)
+
+    # Press 'q' to exit
+    if cv2.waitKey(1) & 0xFF == ord("q"):
+        break
+
+cap.release()
+cv2.destroyAllWindows()