R-CNN源代码

R-CNN源代码是一个基于深度学习的目标检测算法，用于识别图像中的物体。下面是R-CNN的Python源代码示例：

import cv2
import numpy as np
import matplotlib.pyplot as plt

# Load the input image
image = cv2.imread('input_image.jpg')

# Load the pre-trained network for object detection
net = cv2.dnn.readNetFromCaffe('deploy.prototxt', 'model.caffemodel')

# Define the class labels
class_labels = ['background', 'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor']

# Create a blob from the input image
blob = cv2.dnn.blobFromImage(image, size=(300, 300), swapRB=True)

# Set the input to the network
net.setInput(blob)

# Run the forward pass to get the output of the model
detections = net.forward()

# Loop over the detections
for i in range(detections.shape[2]):
    confidence = detections[0, 0, i, 2]

    # Check if the detection has a high enough confidence
    if confidence > 0.5:
        class_id = int(detections[0, 0, i, 1])

        # Get the coordinates of the bounding box
        box = detections[0, 0, i, 3:7] * np.array([image.shape[1], image.shape[0], image.shape[1], image.shape[0]])
        x1, y1, x2, y2 = box.astype('int')

        # Draw the bounding box and class label
        cv2.rectangle(image, (x1, y1), (x2, y2), (0, 255, 0), thickness=2)
        cv2.putText(image, class_labels[class_id], (x1, y1-10), cv2.FONT_HERSHEY_SIMPLEX, fontScale=0.5, color=(0, 255, 0), thickness=2)

# Show the output image
plt.imshow(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
plt.show()

该代码使用OpenCV和Caffe实现了R-CNN目标检测算法。在这个代码示例中，我们首先加载输入图像，然后加载预先训练好的网络模型。我们创建一个blob对象，然后将其输入到网络中进行前向传递，并获得模型的输出结果。最后，我们遍历输出结果，将置信度高于0.5的检测框绘制在图像上，并标注检测到的物体的类别。