Initial commit

Divers/jetson/inference_avec_distance.py

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+import pyrealsense2 as rs
+import cv2
+import numpy as np
+import jetson.inference
+import jetson.utils
+import time
+
+net=jetson.inference.detectNet("SSD-Inception-v2", threshold=0.5)
+#net=jetson.inference.detectNet("SSD-MobileNet-v2", threshold=0.5)
+display=jetson.utils.videoOutput("display://0")
+
+pipeline=rs.pipeline()
+config=rs.config()
+
+config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 15)
+config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 15)
+
+align_to = rs.stream.color
+align = rs.align(align_to)
+
+pipeline.start(config)
+
+while True:
+
+    frames=pipeline.wait_for_frames()
+
+    aligned_frames = align.process(frames)
+
+    depth_frame=aligned_frames.get_depth_frame()
+    color_frame=aligned_frames.get_color_frame()
+
+    if not depth_frame or not color_frame:
+        continue
+
+    depth_image=np.array(depth_frame.get_data())
+    color_image=np.array(color_frame.get_data())
+
+    depth_colormap=cv2.applyColorMap(cv2.convertScaleAbs(depth_image, alpha=0.03), cv2.COLORMAP_JET)
+
+    start=time.time()
+    cuda_image=jetson.utils.cudaFromNumpy(color_image)    
+    detections=net.Detect(cuda_image, color_image.shape[1], color_image.shape[0])
+    
+    print("#######################################")
+    print("Temps", time.time()-start)
+    for detection in detections:
+        print(detection)
+        x, y=detection.Center
+        x1=int(x-detection.Width/2)
+        y1=int(y-detection.Height/2)
+        x2=int(x+detection.Width/2)
+        y2=int(y+detection.Height/2)
+        if detection.ClassID==1:
+            cv2.rectangle(color_image, (x1, y1), (x2, y2), (255, 0, 0), 2)
+            dist=depth_frame.get_distance(int(x), int(y))
+            if dist<1:
+                msg="{:2.0f} cm".format(dist*100)
+            else:
+                msg="{:4.2f} m".format(dist)
+            cv2.putText(color_image, msg, (int(x), int(y)), cv2.FONT_HERSHEY_DUPLEX, 1, (255, 255, 255), 1, cv2.LINE_AA)
+            
+    cv2.imshow('RealSense1', depth_colormap)
+    cv2.imshow('RealSense2', color_image)
+
+    display.Render(cuda_image)
+    cuda_image=jetson.utils.cudaToNumpy(cuda_image)
+    #display.SetStatus("Object Detection | Network {:.0f} FPS".format(net.GetNetworkFPS()))
+    cv2.imshow('cuda_image', cuda_image)
+    key=cv2.waitKey(1)&0xFF
+    if key==ord('q'):
+        pipeline.stop()
+        quit()