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import os
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import cv2
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import numpy as np
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import sys
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import json
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def inpaint():
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cap = cv2.VideoCapture(0)
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ok, src = cap.read()
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# cap默认拍摄640*480的照片,现在破坏图片
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for i in range(200, 300):
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src[200, i] = 255
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src[200 + 1, i] = 255
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src[200 - 1, i] = 255
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for i in range(150, 250):
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src[i, 250] = 255
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src[i, 250 + 1] = 255
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src[i, 250 - 1] = 255
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cap.release()
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if ok:
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img = src
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height = img.shape[0]
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width = img.shape[1]
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#选择要修复的区域用paint表示
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paint = np.zeros((height, width, 1), np.uint8)
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for i in range(200, 300):
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paint[200, i] = 255
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paint[200 + 1, i] = 255
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paint[200 - 1, i] = 255
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for i in range(100, 300):
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paint[i, 250] = 255
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paint[i, 250 + 1] = 255
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paint[i, 250 - 1] = 255
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result = cv2.inpaint(img, paint, 3, cv2.INPAINT_TELEA)
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img = np.hstack([src, result])
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cv2.imshow("source and result", img)
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# 等待关闭
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cv2.waitKey(0)
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def singleScaleRetinex(img, sigma):
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retinex = np.log10(img) - np.log10(cv2.GaussianBlur(img, (0, 0), sigma))
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return retinex
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def multiScaleRetinex(img, sigma_list):
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retinex = np.zeros_like(img)
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for sigma in sigma_list:
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retinex += singleScaleRetinex(img, sigma)
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retinex = retinex / len(sigma_list)
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return retinex
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def colorRestoration(img, alpha, beta):
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img_sum = np.sum(img, axis=2, keepdims=True)
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color_restoration = beta * (np.log10(alpha * img) - np.log10(img_sum))
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return color_restoration
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def simplestColorBalance(img, low_clip, high_clip):
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total = img.shape[0] * img.shape[1]
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for i in range(img.shape[2]):
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unique, counts = np.unique(img[:, :, i], return_counts=True)
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current = 0
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for u, c in zip(unique, counts):
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if float(current) / total < low_clip:
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low_val = u
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if float(current) / total < high_clip:
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high_val = u
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current += c
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img[:, :, i] = np.maximum(np.minimum(img[:, :, i], high_val), low_val)
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return img
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def MSRCR(img, sigma_list, G, b, alpha, beta, low_clip, high_clip):
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img = np.float64(img) + 1.0
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img_retinex = multiScaleRetinex(img, sigma_list)
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img_color = colorRestoration(img, alpha, beta)
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img_msrcr = G * (img_retinex * img_color + b)
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for i in range(img_msrcr.shape[2]):
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img_msrcr[:, :, i] = (img_msrcr[:, :, i] - np.min(img_msrcr[:, :, i])) / \
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(np.max(img_msrcr[:, :, i]) - np.min(img_msrcr[:, :, i])) * \
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255
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img_msrcr = np.uint8(np.minimum(np.maximum(img_msrcr, 0), 255))
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img_msrcr = simplestColorBalance(img_msrcr, low_clip, high_clip)
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return img_msrcr
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def automatedMSRCR(img, sigma_list):
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img = np.float64(img) + 1.0
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img_retinex = multiScaleRetinex(img, sigma_list)
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for i in range(img_retinex.shape[2]):
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unique, count = np.unique(np.int32(img_retinex[:, :, i] * 100), return_counts=True)
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for u, c in zip(unique, count):
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if u == 0:
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zero_count = c
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break
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low_val = unique[0] / 100.0
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high_val = unique[-1] / 100.0
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for u, c in zip(unique, count):
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if u < 0 and c < zero_count * 0.1:
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low_val = u / 100.0
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if u > 0 and c < zero_count * 0.1:
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high_val = u / 100.0
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break
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img_retinex[:, :, i] = np.maximum(np.minimum(img_retinex[:, :, i], high_val), low_val)
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img_retinex[:, :, i] = (img_retinex[:, :, i] - np.min(img_retinex[:, :, i])) / \
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(np.max(img_retinex[:, :, i]) - np.min(img_retinex[:, :, i])) \
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* 255
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img_retinex = np.uint8(img_retinex)
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return img_retinex
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def MSRCP(img, sigma_list, low_clip, high_clip):
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img = np.float64(img) + 1.0
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intensity = np.sum(img, axis=2) / img.shape[2]
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retinex = multiScaleRetinex(intensity, sigma_list)
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intensity = np.expand_dims(intensity, 2)
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retinex = np.expand_dims(retinex, 2)
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intensity1 = simplestColorBalance(retinex, low_clip, high_clip)
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intensity1 = (intensity1 - np.min(intensity1)) / \
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(np.max(intensity1) - np.min(intensity1)) * \
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255.0 + 1.0
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img_msrcp = np.zeros_like(img)
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for y in range(img_msrcp.shape[0]):
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for x in range(img_msrcp.shape[1]):
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B = np.max(img[y, x])
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A = np.minimum(256.0 / B, intensity1[y, x, 0] / intensity[y, x, 0])
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img_msrcp[y, x, 0] = A * img[y, x, 0]
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img_msrcp[y, x, 1] = A * img[y, x, 1]
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img_msrcp[y, x, 2] = A * img[y, x, 2]
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img_msrcp = np.uint8(img_msrcp - 1.0)
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return img_msrcp
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def retinex():
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with open('config.json', 'r') as f:
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config = json.load(f)
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cap = cv2.VideoCapture(0)
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ok, src = cap.read()
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img = src
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cap.release()
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if ok:
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print("选择类别：1..彩色恢复多尺度Retinex（MSRCR） 2.彩色恢复多尺度Retinex（MSRCP） 3.彩色恢复多尺度Retinex(AMSRCR)")
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print("#Retinex对清晰的图像处理效果不佳，适合用来处理光线不好，有雾等这些类的图片#")
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myinput = input()
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if myinput == '1':
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img_msrcr = MSRCR(
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img,
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config['sigma_list'],
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config['G'],
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config['b'],
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config['alpha'],
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config['beta'],
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config['low_clip'],
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config['high_clip']
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)
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img = np.hstack([src, img_msrcr])
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cv2.imshow('resourse and result', img)
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cv2.waitKey(0)
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elif myinput == '3':
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img_amsrcr = automatedMSRCR(
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img,
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config['sigma_list']
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)
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img = np.hstack([src, img_amsrcr])
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cv2.imshow('resourse and result', img)
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cv2.waitKey(0)
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elif myinput == '2':
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img_msrcp = MSRCP(
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img,
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config['sigma_list'],
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config['low_clip'],
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config['high_clip']
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)
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img = np.hstack([src, img_msrcp])
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cv2.imshow('resourse and result', img)
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cv2.waitKey(0)
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else:
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print("Wrong input!")
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def main():
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print("选择图像修复的算法 1.opencv中inpaint图像修复 2.模仿人类视觉系统的Retinex算法")
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myinput = input()
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if myinput == '1':
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inpaint()
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elif myinput == '2':
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retinex()
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else:
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print("Wrong input!") |
