55 lines
1.5 KiB
Python
55 lines
1.5 KiB
Python
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import pyrealsense2 as rs
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import cv2
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import numpy as np
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import jetson.inference
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import jetson.utils
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import time
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net=jetson.inference.detectNet("SSD-Inception-v2", threshold=0.5)
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#net=jetson.inference.detectNet("SSD-MobileNet-v2", threshold=0.5)
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display=jetson.utils.videoOutput("display://0")
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pipeline=rs.pipeline()
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config=rs.config()
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config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 15)
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config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 15)
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align_to = rs.stream.color
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align = rs.align(align_to)
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pipeline.start(config)
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while True:
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frames=pipeline.wait_for_frames()
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aligned_frames = align.process(frames)
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depth_frame=aligned_frames.get_depth_frame()
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color_frame=aligned_frames.get_color_frame()
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if not depth_frame or not color_frame:
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continue
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depth_image=np.array(depth_frame.get_data())
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color_image=np.array(color_frame.get_data())
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depth_colormap=cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
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start=time.time()
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cuda_image=jetson.utils.cudaFromNumpy(color_image)
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detections=net.Detect(cuda_image, color_image.shape[1], color_image.shape[0])
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print("Temps", time.time()-start)
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display.Render(cuda_image)
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cuda_image=jetson.utils.cudaToNumpy(cuda_image)
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cv2.imshow('RealSense1', depth_colormap)
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#cv2.imshow('RealSense2', color_image)
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cv2.imshow('cuda_image', cuda_image)
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key=cv2.waitKey(1)&0xFF
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if key==ord('q'):
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pipeline.stop()
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quit()
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