Python赋能计算机视觉：探索图像处理与分析的全新境界

import cv2

# 读取图像
image = cv2.imread("image.jpg")

# 调整亮度
image = cv2.addWeighted(image, 1.5, 0, 0)

# 调整对比度
image = cv2.convertScaleAbs(image, alpha=1.5, beta=0)

# 调整饱和度
image = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
image[:, :, 1] = image[:, :, 1] * 1.5
image = cv2.cvtColor(image, cv2.COLOR_HSV2BGR)

# 保存图像
cv2.imwrite("image_enhanced.jpg", image)

import cv2

# 读取图像
image = cv2.imread("image.jpg")

# 灰度化
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 高斯滤波
image = cv2.GaussianBlur(image, (5, 5), 0)

# Canny边缘检测
edges = cv2.Canny(image, 100, 200)

# 轮廓检测
contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHaiN_APPROX_SIMPLE)

# 绘制轮廓
cv2.drawContours(image, contours, -1, (0, 255, 0), 2)

# 保存图像
cv2.imwrite("image_segmented.jpg", image)

import cv2
import numpy as np

# 读取图像
image = cv2.imread("image.jpg")

# 灰度化
image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

# 二值化
image = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY_INV)[1]

# 查找轮廓
contours, _ = cv2.findContours(image, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)

# 识别轮廓
for contour in contours:
x, y, w, h = cv2.boundingRect(contour)
roi = image[y:y+h, x:x+w]

# 使用机器学习模型识别对象
label = model.predict(roi)

# 绘制识别结果
cv2.rectangle(image, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv2.putText(image, label, (x, y-10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 255, 0), 2)

# 保存图像
cv2.imwrite("image_recognized.jpg", image)