帮写一段使用realsense d435i相机的目标检测代码
时间: 2023-11-06 07:05:42 浏览: 47
好的,以下是一段使用realsense d435i相机进行目标检测的代码:
```python
import pyrealsense2 as rs
import cv2
# Load YOLOv3 object detection model
net = cv2.dnn.readNet("yolov3.weights", "yolov3.cfg")
classes = []
with open("coco.names", "r") as f:
classes = [line.strip() for line in f.readlines()]
layer_names = net.getLayerNames()
output_layers = [layer_names[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# Configure depth and color streams
pipeline = rs.pipeline()
config = rs.config()
config.enable_stream(rs.stream.depth, 640, 480, rs.format.z16, 30)
config.enable_stream(rs.stream.color, 640, 480, rs.format.bgr8, 30)
# Start streaming
pipeline.start(config)
try:
while True:
# Wait for a coherent pair of frames
frames = pipeline.wait_for_frames()
depth_frame = frames.get_depth_frame()
color_frame = frames.get_color_frame()
# Convert images to numpy arrays
depth_image = np.asanyarray(depth_frame.get_data())
color_image = np.asanyarray(color_frame.get_data())
# Create blob from color image for object detection
blob = cv2.dnn.blobFromImage(cv2.resize(color_image, (416, 416)), 0.00392, (416, 416), (0, 0, 0), True, crop=False)
# Perform object detection
net.setInput(blob)
outs = net.forward(output_layers)
# Get object detection results
class_ids = []
confidences = []
boxes = []
for out in outs:
for detection in out:
scores = detection[5:]
class_id = np.argmax(scores)
confidence = scores[class_id]
if confidence > 0.5:
# Object detected
center_x = int(detection[0] * 640)
center_y = int(detection[1] * 480)
w = int(detection[2] * 640)
h = int(detection[3] * 480)
x = center_x - w // 2
y = center_y - h // 2
class_ids.append(class_id)
confidences.append(float(confidence))
boxes.append([x, y, w, h])
# Draw object detection results on color image
indexes = cv2.dnn.NMSBoxes(boxes, confidences, 0.5, 0.4)
font = cv2.FONT_HERSHEY_PLAIN
colors = np.random.uniform(0, 255, size=(len(classes), 3))
for i in range(len(boxes)):
if i in indexes:
x, y, w, h = boxes[i]
label = str(classes[class_ids[i]])
color = colors[class_ids[i]]
cv2.rectangle(color_image, (x, y), (x + w, y + h), color, 2)
cv2.putText(color_image, label, (x, y + 30), font, 2, color, 2)
# Show images
cv2.imshow("Color Image", color_image)
cv2.imshow("Depth Image", depth_image)
# Exit on ESC key
if cv2.waitKey(1) == 27:
break
finally:
# Stop streaming
pipeline.stop()
cv2.destroyAllWindows()
```
这段代码使用了OpenCV和pyrealsense2库来获取realsense d435i相机的深度和颜色图像,并利用YOLOv3模型进行目标检测。检测到的物体在彩色图像上用矩形框标出,并在框上显示识别出的物体类别。
相关推荐
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)
![](https://csdnimg.cn/download_wenku/file_type_ask_c1.png)