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import cv2
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import numpy as np
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import math
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# RGB色彩空间
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def Rgb(path):
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img = cv2.imread(path, 1)
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b = img[:, :, 0]
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g = img[:, :, 1]
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r = img[:, :, 2]
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cv2.imwrite('./media/RGB/b.jpg', b)
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cv2.imwrite('./media/RGB/g.jpg', g)
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cv2.imwrite('./media/RGB/r.jpg', r)
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# HSV色彩空间
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def Hsv(path):
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img = cv2.imread(path, 1)
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hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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h = hsv[:, :, 0]
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s = hsv[:, :, 1]
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v = hsv[:, :, 2]
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cv2.imwrite("./media/HSV/h.jpg", h)
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cv2.imwrite("./media/HSV/s.jpg", s)
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cv2.imwrite("./media/HSV/v.jpg", v)
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def Un(path):
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img = cv2.imread(path, 0)
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result = ~img
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cv2.imwrite("./media/UN/un.jpg", result)
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def Add(paths):
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# 只做了两个图像叠加，多个怎么做
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img1 = cv2.imread(paths[0], 1)
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img2 = cv2.imread(paths[1], 1)
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img = cv2.add(img1, img2)
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cv2.imwrite("./media/ADD/add.jpg", img)
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def Subtract(paths):
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img1 = cv2.imread(paths[0], 1)
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img2 = cv2.imread(paths[1], 1)
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img = cv2.subtract(img1, img2)
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cv2.imwrite("./media/SUBTRACT/subtract.jpg", img)
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# 形态学操作
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def OpenAndClose(path):
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# 二值转换
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img = cv2.imread(path, 1)
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gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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retval, src = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
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# 定义十字形结构元素
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kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
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# 对二值图进行开运算和闭运算操作
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im_op = cv2.morphologyEx(src, cv2.MORPH_OPEN, kernel)
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im_cl = cv2.morphologyEx(src, cv2.MORPH_CLOSE, kernel)
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cv2.imwrite('./media/OPANDCL/op.png', im_op)
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cv2.imwrite('./media/OPANDCL/cl.png', im_cl)
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# 边缘检测和图像增强
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def Sharp(path):
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CRH = cv2.imread(path, 1)
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gradient = np.zeros_like(CRH)
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CRH = CRH.astype('float')
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h, w = CRH.shape[:2]
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for x in range(h - 1):
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for y in range(w - 1):
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gx = abs(CRH[x + 1, y] - CRH[x, y])
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gy = abs(CRH[x, y + 1] - CRH[x, y])
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gradient[x, y] = gx + gy
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sharp = CRH + gradient
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sharp = np.where(sharp > 255, 255, sharp)
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sharp = np.where(sharp < 0, 0, sharp)
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gradient = gradient.astype('uint8')
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sharp = sharp.astype('uint8')
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cv2.imwrite('./media/SHARP/gradient.png', gradient)
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cv2.imwrite('./media/SHARP/sharp.png', sharp)
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# 镜像
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def Turn(path):
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img = cv2.imread(path)
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# 水平镜像
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horizontal = cv2.flip(img, 1, dst=None)
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# 垂直镜像
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vertical = cv2.flip(img, 0, dst=None)
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# 对角镜像
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cross = cv2.flip(img, -1, dst=None)
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cv2.imwrite("./media/TURN/horizontal.jpg", horizontal)
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cv2.imwrite("./media/TURN/vertical.jpg", vertical)
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cv2.imwrite("./media/TURN/cross.jpg", cross)
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# 腐蚀
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def Erosion(path):
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src = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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# 10x10的交叉型结构元
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kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
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erosion = cv2.erode(src, kernel)
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cv2.imwrite("./media/EROSION/erosion.jpg", erosion)
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# 膨胀
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def Dilation(path):
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src = cv2.imread(path, cv2.IMREAD_UNCHANGED)
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# 10x10的交叉结构元
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kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
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dilation = cv2.dilate(src, kernel)
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cv2.imwrite("./media/DILATION/dilation.png", dilation)
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# 噪声及滤波
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def Noise(path):
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image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
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output = np.zeros(image.shape, np.uint8)
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output2 = np.zeros(image.shape, np.uint8)
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# 叠加噪声
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for i in range(image.shape[0]):
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for j in range(image.shape[1]):
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if image[i][j] < 40:
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# 添加食盐噪声
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output[i][j] = 255
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elif image[i][j] > 200:
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# 添加胡椒噪声
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output[i][j] = 0
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# 不添加噪声
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else:
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output[i][j] = image[i][j]
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# 均值滤波
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for i in range(output.shape[0]):
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for j in range(output.shape[1]):
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ji = 1.0
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for n in range(-1, 2):
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if 0 <= i < output.shape[0] and 0 <= j + n < output.shape[1]:
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ji = ji * output[i][j + n]
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output2[i][j] = int(ji ** (1 / 3))
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cv2.imwrite("./media/NOISE/noise.jpg", output)
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cv2.imwrite("./media/NOISE/meanFilter.jpg", output2)
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# # 扩展平移缩放
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# def moveandchange(path):
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# img = cv2.imread(path, 1)
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# l, w, h = img.shape
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# # 扩展，使用双线性插值法
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# img = cv2.resize(img, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_LINEAR)
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# height, width, channel = img.shape
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# # 移动
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# M = np.float32([[1, 0, 30], [0, 1, 60]])
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# img = cv2.warpAffine(img, M, (width, height))
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# # 旋转
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# rows, cols, depth = img.shape
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# M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 45, 1)
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# dst = cv2.warpAffine(img, M, (width, height))
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#
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# cv2.imwrite('./media/CHANGE/out.png', dst)
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# # 仿射变换
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# def affine(path):
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# img = cv2.imread(path)
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# rows, cols = img.shape[: 2]
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# # 设置图像仿射变化矩阵
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# post1 = np.float32([[50, 50], [200, 50], [50, 200]])
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# post2 = np.float32([[10, 100], [200, 50], [100, 250]])
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# M = cv2.getAffineTransform(post1, post2)
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# # 图像仿射变换
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# result = cv2.warpAffine(img, M, (rows, cols))
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# cv2.imwrite("./media/AFFINE/affine.jpg", result)
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