68 lines
2.3 KiB
Python
68 lines
2.3 KiB
Python
# -*- coding: utf-8 -*-
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# @Time : 2024/4/15 9:39
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# @Author : len
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# @File : 感兴趣区域提取2.py
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# @Software: PyCharm
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# @Comment :
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import os
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import cv2
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import numpy as np
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from matplotlib import pyplot as plt
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plt.rcParams['font.sans-serif'] = ['SimHei']
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# 定义一个调整后的图像处理函数来改善屏幕检测
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def process_image_adjusted(image_path):
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# 加载图片
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image = cv2.imread(image_path)
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# 将图片转换成灰度图
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 应用高斯模糊，减少噪点，提升边缘检测效果
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blurred = cv2.GaussianBlur(gray, (5, 5), 0)
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# 自适应阈值化创建二值图像
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threshold = cv2.adaptiveThreshold(blurred, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C,
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cv2.THRESH_BINARY_INV, 11, 2)
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# 从阈值化的图像中找轮廓
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contours, _ = cv2.findContours(threshold, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
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# 根据轮廓面积排序，并移除面积较小的轮廓
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contours = sorted(contours, key=cv2.contourArea, reverse=True)
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large_contours = [cnt for cnt in contours if cv2.contourArea(cnt) > 1000]
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screen_contour = None
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for contour in large_contours:
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# 将轮廓近似为多边形
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perimeter = cv2.arcLength(contour, True)
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approximation = cv2.approxPolyDP(contour, 0.02 * perimeter, True)
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# 如果多边形有4条边，我们假设它是屏幕
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if len(approximation) == 4:
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screen_contour = approximation
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break
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# 在图片上绘制轮廓
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if screen_contour is not None:
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cv2.drawContours(image, [screen_contour], -1, (0, 255, 0), 3)
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screen_image = cv2.polylines(image, [screen_contour], True, (0, 255, 0), 3)
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else:
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screen_image = image
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# 展示结果
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plt.imshow(cv2.cvtColor(screen_image, cv2.COLOR_BGR2RGB))
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plt.title('调整后的屏幕检测')
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plt.axis('off') # 关闭坐标轴显示
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plt.show()
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# 设置文件夹路径
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folder_path = './data/pingmu' # 将此路径替换为你的图片文件夹路径
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# 遍历文件夹中的所有文件
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for filename in os.listdir(folder_path):
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if filename.lower().endswith(('.png', '.jpg', '.jpeg')): # 检查文件扩展名
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file_path = os.path.join(folder_path, filename) # 获取文件完整路径
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process_image_adjusted(file_path) |