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Regino
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Parent(s):
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sbdhf
Browse files- requirements.txt +4 -4
- src/streamlit_app.py +28 -15
requirements.txt
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# requirements.txt
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streamlit
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opencv-python
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tensorflow
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numpy
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streamlit-webrtc
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streamlit
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opencv-python
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tensorflow-cpu # <-- Use this if your Hugging Face Space is CPU-only. Otherwise, keep 'tensorflow'.
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numpy
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streamlit-webrtc
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av
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Pillow
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src/streamlit_app.py
CHANGED
@@ -80,7 +80,7 @@ class EmotionDetector(VideoTransformerBase):
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def transform(self, frame: av.VideoFrame) -> np.ndarray:
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# Convert av.VideoFrame to NumPy array.
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# Requesting "bgr24" format directly from `av` to align with OpenCV's default.
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img_bgr = frame.to_ndarray(format="bgr24")
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# Convert to grayscale for face detection and emotion prediction
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gray_frame = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
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x_orig = int(x / FACE_DETECTION_DOWNSCALE)
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y_orig = int(y / FACE_DETECTION_DOWNSCALE)
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w_orig = int(w / FACE_DETECTION_DOWNSCALE)
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h_orig = int(h / FACE_DETECTION_DOWNSCALE)
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original_faces.append((x_orig, y_orig, w_orig, h_orig))
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# Process each detected face
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mode=WebRtcMode.SENDRECV, # Send video from client, receive processed video from server
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video_processor_factory=lambda: EmotionDetector(model, face_detector),
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media_stream_constraints={"video": True, "audio": False}, # Only video, no audio
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)
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# Provide feedback based on the stream state
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def transform(self, frame: av.VideoFrame) -> np.ndarray:
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# Convert av.VideoFrame to NumPy array.
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# Requesting "bgr24" format directly from `av` to align with OpenCV's default.
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img_bgr = frame.to_ndarray(format="bgr24")
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# Convert to grayscale for face detection and emotion prediction
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gray_frame = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
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x_orig = int(x / FACE_DETECTION_DOWNSCALE)
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y_orig = int(y / FACE_DETECTION_DOWNSCALE)
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w_orig = int(w / FACE_DETECTION_DOWNSCALE)
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h_orig = int(h / FACE_DETECTION_DOWNSCALE)
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original_faces.append((x_orig, y_orig, w_orig, h_orig))
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# Process each detected face
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mode=WebRtcMode.SENDRECV, # Send video from client, receive processed video from server
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video_processor_factory=lambda: EmotionDetector(model, face_detector),
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media_stream_constraints={"video": True, "audio": False}, # Only video, no audio
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# Temporarily set async_processing to False for debugging.
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# If this works, it points to an asyncio interaction issue. For better performance,
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# you'd ideally re-enable async_processing=True later.
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async_processing=False,
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# Optional: tries to auto-start. Can comment out if you prefer manual start.
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# desired_playing_state={"playing": True},
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# --- ENHANCED RTC CONFIGURATION ---
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# Providing a robust list of public STUN servers for better NAT traversal
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rtc_configuration={
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"iceServers": [
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{"urls": ["stun:stun.l.google.com:19302"]},
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{"urls": ["stun:stun1.l.google.com:19302"]},
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{"urls": ["stun:stun2.l.google.com:19302"]},
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{"urls": ["stun:stun3.l.google.com:19302"]},
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{"urls": ["stun:stun4.l.google.com:19302"]},
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{"urls": ["stun:stun.services.mozilla.com"]},
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{"urls": ["stun:global.stun.twilio.com:3478"]},
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{"urls": ["stun:stun.nextcloud.com:3478"]}, # Added another
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{"urls": ["stun:stun.schlund.de"]}, # Added another
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]
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},
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# For more detailed debugging in the browser console, uncomment the line below.
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# log_level="debug",
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)
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# Provide feedback based on the stream state
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