From 5b13ea5a25c0032a490f1a31a80040e0b97aa1d4 Mon Sep 17 00:00:00 2001 From: pgw247brm Date: Sat, 1 Jul 2023 22:16:21 +0800 Subject: [PATCH] ADD file via upload --- picture_fun.py | 234 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 234 insertions(+) create mode 100644 picture_fun.py diff --git a/picture_fun.py b/picture_fun.py new file mode 100644 index 0000000..c35faf1 --- /dev/null +++ b/picture_fun.py @@ -0,0 +1,234 @@ +import math + +import cv2 +import numpy as np + +####################灰度图和二值化######################################## + + + +def gray_picture(img): + img_gray = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY) + return img_gray + +def erzhihua(image): + image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + rst = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY) + return rst[1] + +##################加噪########################################################### +def add_noisy(image, snr): #椒盐彩色 + result = image.copy() + size = result.size + n1= int(size*(1-snr)) + print(n1) + w, h = image.shape[:2] + for i in range(n1): + # 宽和高的范围内生成一个随机值，模拟表x,y坐标 + x = np.random.randint(1, w) + y = np.random.randint(1, h) + if np.random.randint(0, 2) == 0: + # 生成白色噪声（盐噪声） + result[x, y] = 0 + else: + # 生成黑色噪声（椒噪声） + result[x, y] = 255 + return result + +""" +def add_noisy(image, n): #椒盐彩色 + result = image.copy() + size = result.size + n= int(n) + w, h = image.shape[:2] + for i in range(n): + # 宽和高的范围内生成一个随机值，模拟表x,y坐标 + x = np.random.randint(1, w) + y = np.random.randint(1, h) + if np.random.randint(0, 2) == 0: + # 生成白色噪声（盐噪声） + result[x, y] = 0 + else: + # 生成黑色噪声（椒噪声） + result[x, y] = 255 + return result +""" + +####################彩色图像添加高斯噪声####################################### +""" +def add_noise(image,mean,var): + img = np.array(image / 255, dtype=float) + noise = np.random.normal(mean, var ** 0.5, img.shape) + out_img = img + noise + if out_img.min() < 0: + low_clip = -1 + else: + low_clip = 0 + out_img = np.clip(out_img, low_clip, 1.0) + out_img = out_img * 255 + return out_img +""" +def add_noise(image, mean=0.0, var=0.1): + img = np.array(image / 255, dtype=float) + noise = np.random.normal(mean, var, image.shape) + out_img = img + noise + if out_img.min() < 0: + low_clip = -1. + else: + low_clip = 0. + gauss_noise = np.clip(out_img, low_clip, 1.0) + gauss_noise = np.uint8(out_img * 255) + return out_img + +################图像锐化##################### +def lap_9(image): #拉普拉斯变化 + lap_9 = np.array([[-1, -1, -1], [-1, 9, -1], [-1, -1, -1]]) + # 拉普拉斯9的锐化 + image = cv2.filter2D(image, cv2.CV_8U, lap_9) + return image + +def gama_transfer(img,power1=1.5): #伽马变化 + if len(img.shape) == 3: + img= cv2.cvtColor(img,cv2.CV_8U) + img = 255*np.power(img/255,power1) + img = np.around(img) + img[img>255] = 255 + out_img = img.astype(np.uint8) + return out_img + + +def get_imghist(img): #直方图均衡 + # 判断图像是否为三通道； + if len(img.shape) == 3: + img = cv2.cvtColor(img, cv2.CV_8U) + # 无 Mask，256个bins,取值范围为[0,255] + hist = cv2.calcHist([img], [0], None, [256], [0, 255]) + return hist + + +def cal_equalhist(img): + if len(img.shape) == 3: + img = cv2.cvtColor(img, cv2.CV_8U) + h, w = img.shape[:2] + grathist = get_imghist(img) + + zerosumMoment = np.zeros([256], np.uint32) + for p in range(256): + if p == 0: + zerosumMoment[p] = grathist[0] + else: + zerosumMoment[p] = zerosumMoment[p - 1] + grathist[p] + + output_q = np.zeros([256], np.uint8) + cofficient = 256.0 / (h * w) + for p in range(256): + q = cofficient * float(zerosumMoment[p]) - 1 + if q >= 0: + output_q[p] = math.floor(q) + else: + output_q[p] = 0 + + equalhistimage = np.zeros(img.shape, np.uint8) + for i in range(h): + for j in range(w): + equalhistimage[i][j] = output_q[img[i][j]] + + return equalhistimage +""" +img = cv2.imread('img.jpg',1) +(b,g,r) = cv2.split(img) #通道分解 +bH = cv2.equalizeHist(b) +gH = cv2.equalizeHist(g) +rH = cv2.equalizeHist(r) +result = cv2.merge((bH,gH,rH),)#通道合成 +res = np.hstack((img,result)) +cv2.imshow('dst',res) +cv2.waitKey(0) +""" +################图像去噪#################################################### +def boxFilterfun(image): #方波滤波 + image=cv2.boxFilter(image,-1,(2,2),normalize=0) + return image + + + +def medianBlurfun(image): #中值滤波 + image=cv2.medianBlur(image,3) + return image + + + +def bilateralFilterfun(image): #双边滤波 + image=cv2.bilateralFilter(image,25,100,100) + return image + + +def GaussianBlurfun(image): #高斯滤波 + image=cv2.GaussianBlur(image,(5,5),0,0) + return image + + +def blurfun(image): #均值滤波 + image==cv2.blur(image,(5,5)) + return image + + +###############图像翻转#################################################### +def flipfun(image,x): #图像 水平翻转:0 垂直翻转:1 沿xy轴翻转:-1 + image = cv2.flip(image,x) + return image + +def fangsuo(image,x): + image = cv2.resize(image, (0, 0), fx=x, fy=x, interpolation=cv2.INTER_NEAREST) + return image + + +##############按位取反################################################### +def bitwise_notfun(image): + image = cv2.bitwise_not(image) + return image + + +##############轮廓检测#################################################### +def morphologyExfun(image): + kernel = np.ones((3, 3), dtype=np.uint8) + image_gradient = cv2.morphologyEx(image, cv2.MORPH_GRADIENT, kernel) + return image_gradient + + +#############sift检测####################################################### +def sift_fun(image): + sift = cv2.SIFT_create() + kps = sift.detect(image) + image_sift = cv2.drawKeypoints(image, kps, None, -1, cv2.DrawMatchesFlags_DEFAULT) + return image_sift + + +##################平均值池化################################################# +def average_poolingfun(img, G=4): + out = img.copy() + H, W, C = img.shape + Nh = int(H / G) + Nw = int(W / G) + for y in range(Nh): + for x in range(Nw): + for c in range(C): + out[G * y:G * (y + 1), G * x:G * (x + 1), c] = np.mean( + out[G * y:G * (y + 1), G * x:G * (x + 1), c]).astype(np.int) + return out + +##################################修复#### +def xiufu(image): + _, mask1 = cv2.threshold(image, 245, 255, cv2.THRESH_BINARY) + k = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3)) + mask1 = cv2.dilate(mask1, k) + result1 = cv2.inpaint(image, mask1[:, :, -1], 5, cv2.INPAINT_NS) + return result1 + +if __name__ == '__main__': + image = cv2.imread("inpaint2.jpg") + cv2.imshow(' ',image) + newimage=xiufu(image) + cv2.imshow(' ',newimage) + cv2.waitKey(0) +