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https://github.com/Kalit31/AR-Video-Streaming-over-WebRTC.git
synced 2025-09-27 03:35:55 +08:00
Stuck with mediapipe issues on Jetson
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@@ -10,8 +10,10 @@ import struct
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mp_face_mesh = mp.solutions.face_mesh
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face_mesh_videos = mp_face_mesh.FaceMesh(static_image_mode=False, max_num_faces=1, min_detection_confidence=0.5, min_tracking_confidence=0.3)
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eye = cv2.imread('/home/epl/Desktop/WebRTC_research/ar-filters/filter_imgs/eye.jpg')
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mouth = cv2.imread('/home/epl/Desktop/WebRTC_research/ar-filters/filter_imgs/smile.png')
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# eye = cv2.imread('/home/epl/Desktop/WebRTC_research/ar-filters/filter_imgs/eye.jpg')
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# mouth = cv2.imread('/home/epl/Desktop/WebRTC_research/ar-filters/filter_imgs/smile.png')
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eye = cv2.imread('/home/kalit/Desktop/GeorgiaTech/Fall_2024/CS_8903/WebRTC_research/ar-filters/filter_imgs/eye.jpg')
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mouth = cv2.imread('/home/kalit/Desktop/GeorgiaTech/Fall_2024/CS_8903/WebRTC_research/ar-filters/filter_imgs/smile.png')
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def detectFacialLandmarks(image, face_mesh):
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return face_mesh.process(image[:,:,::-1])
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@@ -121,7 +123,7 @@ def main():
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# output_file_path = 'output.jpg'
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# with open(output_file_path, 'wb') as f:
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# f.write(buffer)
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print("Sending back processed image")
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# print("Sending back processed image")
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conn.sendall(struct.pack('!I', len(buffer)) + buffer.tobytes())
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conn.close()
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@@ -114,7 +114,7 @@ func OverlayARFilter(conn net.Conn, frame *astiav.Frame) (*astiav.Frame, error)
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return frame, err
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}
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dumpImageToFile("processed.jpg", processed_image)
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// dumpImageToFile("processed.jpg", processed_image)
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return processed_frame, nil
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}
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@@ -66,3 +66,53 @@ https://cuhksz-inml.github.io/full_scene_volumetric_video_dataset/factsfigures.h
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Forward streams to localhost:5005 to jetson machine's localhost:5005 -
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ssh -L 5005:localhost:5005 -J fastvideo -i ~/.ssh/picluster epl@10.100.1.165
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Jetson: 4 CPU cores ARMv8 Processor rev 1
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Device 0: "NVIDIA Tegra X1"
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CUDA Driver Version / Runtime Version 10.0 / 10.0
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CUDA Capability Major/Minor version number: 5.3
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Total amount of global memory: 3962 MBytes (4154626048 bytes)
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( 2) Multiprocessors, (128) CUDA Cores/MP: 256 CUDA Cores
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GPU Max Clock rate: 998 MHz (1.00 GHz)
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Memory Clock rate: 1600 Mhz
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Memory Bus Width: 64-bit
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L2 Cache Size: 262144 bytes
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Maximum Texture Dimension Size (x,y,z) 1D=(65536), 2D=(65536, 65536), 3D=(4096, 4096, 4096)
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Maximum Layered 1D Texture Size, (num) layers 1D=(16384), 2048 layers
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Maximum Layered 2D Texture Size, (num) layers 2D=(16384, 16384), 2048 layers
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Total amount of constant memory: 65536 bytes
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Total amount of shared memory per block: 49152 bytes
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Total number of registers available per block: 32768
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Warp size: 32
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Maximum number of threads per multiprocessor: 2048
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Maximum number of threads per block: 1024
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Max dimension size of a thread block (x,y,z): (1024, 1024, 64)
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Max dimension size of a grid size (x,y,z): (2147483647, 65535, 65535)
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Maximum memory pitch: 2147483647 bytes
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Texture alignment: 512 bytes
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Concurrent copy and kernel execution: Yes with 1 copy engine(s)
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Run time limit on kernels: Yes
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Integrated GPU sharing Host Memory: Yes
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Support host page-locked memory mapping: Yes
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Alignment requirement for Surfaces: Yes
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Device has ECC support: Disabled
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Device supports Unified Addressing (UVA): Yes
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Device supports Compute Preemption: No
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Supports Cooperative Kernel Launch: No
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Supports MultiDevice Co-op Kernel Launch: No
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Device PCI Domain ID / Bus ID / location ID: 0 / 0 / 0
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Compute Mode:
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< Default (multiple host threads can use ::cudaSetDevice() with device simultaneously) >
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deviceQuery, CUDA Driver = CUDART, CUDA Driver Version = 10.0, CUDA Runtime Version = 10.0, NumDevs = 1
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Result = PASS
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Mediapipe issues (GPU + Jetson :( )
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1. https://github.com/google-ai-edge/mediapipe/issues/4017
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2. https://github.com/google-ai-edge/mediapipe/issues/5344#issuecomment-2076742967
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3. https://github.com/google-ai-edge/mediapipe/issues/3353
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4. https://github.com/google-ai-edge/mediapipe/issues/1651#issuecomment-790176010
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5. https://github.com/google-ai-edge/mediapipe/issues/1344
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6. https://github.com/google-ai-edge/mediapipe/issues/5736
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