import os
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import cv2
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from scipy.fftpack import fftshift, fft2
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import numpy as np
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def variance_of_laplacian(image):
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# 计算输入图像的拉普拉斯响应的方差
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return cv2.Laplacian(image, cv2.CV_64F).var()
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def frequency_domain_analysis(gray):
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# 计算傅里叶变换并移动到中心
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f_transform = fftshift(fft2(gray))
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# 计算频谱幅度
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magnitude_spectrum = 20 * np.log(np.abs(f_transform))
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# 计算高频成分的比例
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high_frequency_magnitude = np.sum(magnitude_spectrum > np.mean(magnitude_spectrum))
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total_magnitude = magnitude_spectrum.size
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ratio = high_frequency_magnitude / total_magnitude
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return ratio
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def edge_gradient(gray):
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sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
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sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=3)
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magnitude = np.sqrt(sobelx**2 + sobely**2)
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return np.mean(magnitude)
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def testOne():
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# 加载图像并转换为灰度图
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image = cv2.imread('H:/images/1/20250114193113.jpg')
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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# 计算图像的清晰度分数
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score = variance_of_laplacian(gray)
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ratio = frequency_domain_analysis(gray)
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edge_gradient_score = edge_gradient(gray)
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print("Image clarity score: ", score)
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print("High frequency ratio: ", ratio)
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print("Edge gradient score: ", edge_gradient_score)
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# 根据经验设置阈值来判断图像是否模糊
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threshold = 100 # 这个阈值可以根据实际情况调整
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if score < threshold:
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print("The image is considered blurry.")
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else:
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print("The image is considered clear.")
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def batch_score():
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global name, join
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# 读取文件夹下的所有图像分别计算分数
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folder_path = 'H:/images/2'
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for filename in os.listdir(folder_path):
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if filename.endswith('.jpg') or filename.endswith('.png'):
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image_path = os.path.join(folder_path, filename)
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image = cv2.imread(image_path)
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gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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score = variance_of_laplacian(gray)
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ratio = frequency_domain_analysis(gray)
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edge_gradient_score = edge_gradient(gray)
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# 所有结果保留两位小数
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score = round(score, 2)
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ratio = round(ratio, 2)
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edge_gradient_score = round(edge_gradient_score, 2)
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name = "score=" + str(score) + "ratio=" + str(ratio) + "edge_gradient_score=" + str(
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edge_gradient_score) + ".jpg"
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path_ = folder_path + "/22/"
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# 判断文件夹是否存在，不存在则新建
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if not os.path.exists(path_):
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os.makedirs(path_)
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join = path_ + name
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print(join)
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cv2.imwrite(join, image)
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def cam_score():
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# 调用摄像头计算采集到图像的得分
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camera_index = 2
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print("第二个摄像头索引：" + str(camera_index))
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# 打开摄像头
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capture = cv2.VideoCapture(camera_index, cv2.CAP_DSHOW)
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# 设置分辨率
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capture.set(cv2.CAP_PROP_FRAME_WIDTH, 1921)
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capture.set(cv2.CAP_PROP_FRAME_HEIGHT, 1081)
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# 检查摄像头是否成功打开
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if not capture.isOpened():
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print("无法打开摄像头2")
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exit()
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# 循环读取画面
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while True:
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ret, frame = capture.read()
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if not ret:
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print("无法读取摄像头画面")
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break
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gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
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score = variance_of_laplacian(gray)
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# ratio = frequency_domain_analysis(gray)
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# edge_gradient_score = edge_gradient(gray)
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# 将FPS绘制在图像上
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cv2.putText(frame, f"score={str(score)}"
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, (10, 30), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
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# cv2.putText(frame, f"ratio={str(ratio)}"
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# , (10, 70), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
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# cv2.putText(frame, f"edge_gradient_score={str(edge_gradient_score)}"
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# , (10, 110), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 255), 2, cv2.LINE_AA)
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# 显示画面
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cv2.imshow("Camera", frame)
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# 检测按键，如果按下q键则退出循环
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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# 关闭摄像头
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capture.release()
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# 关闭所有窗口
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cv2.destroyAllWindows()
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if __name__ == '__main__':
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testOne()
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# batch_score()
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# cam_score()
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