opencv python 预训练ENet语义分割

OpenCV Python中有一个预训练的ENet语义分割模型，可以用于对图像进行像素级别的分类和分割。ENet是一种轻量级的神经网络结构，可以在较短的时间内完成图像分割任务，并且具有较高的准确性和效率。

要使用预训练的ENet模型，需要先安装OpenCV和相应的Python包。然后，可以使用以下代码加载模型并对图像进行分割：

import cv2
import numpy as np

# Load the model
net = cv2.dnn.readNet('enet-model.net')

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

# Resize the image to the input size of the network
input_size = (1024, 512)
img_resized = cv2.resize(img, input_size)

# Normalize the image
img_normalized = img_resized / 255.0

# Create a blob from the image
blob = cv2.dnn.blobFromImage(img_normalized, scalefactor=1.0, size=input_size, mean=(0, 0, 0), swapRB=True, crop=False)

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

# Run a forward pass through the network
output = net.forward()

# Get the class probabilities for each pixel in the image
class_probs = output[0]

# Get the class predictions for each pixel in the image
class_preds = np.argmax(class_probs, axis=0)

# Convert the class predictions to a color image
class_colors = [[0, 0, 0], [255, 0, 0], [0, 255, 0], [0, 0, 255]] # Define the colors for each class
class_img = np.zeros((input_size[1], input_size[0], 3), dtype=np.uint8)
for i in range(input_size[1]):
    for j in range(input_size[0]):
        class_img[i, j] = class_colors[class_preds[i, j]]

# Resize the class image back to the original image size
class_img_resized = cv2.resize(class_img, (img.shape[1], img.shape[0]))

# Show the original image and the segmented image side by side
cv2.imshow('Original Image', img)
cv2.imshow('Segmented Image', class_img_resized)
cv2.waitKey(0)

这段代码首先加载了ENet模型，然后加载了一张图像。接下来，将图像缩放到网络的输入大小，并将像素值归一化到0到1的范围内。然后，将图像转换为网络的输入blob，将其设置为网络的输入，并通过网络进行前向传递。输出是每个像素的类概率，可以使用argmax函数获取每个像素的类预测。最后，将类预测转换为彩色图像，并将其调整为原始图像的大小，然后将原始图像和分割图像显示在一起。