from django.shortcuts import render,HttpResponse import numpy as np from io import BytesIO import cv2 import sys import matplotlib.pyplot as plt import numpy # Create your views here. def home(request): return render(request,'home.html') #render用来打开html def login(request): if request.method=='GET': return render(request,'login.html') else: uname=request.POST.get('username') pwd = request.POST.get('password') if uname=='user' and pwd=='password': return render(request, 'home.html') else: return HttpResponse('用户名或密码错误！') '''def login2(request): return render(request,'home.html')''' def photo(request): file_object=request.FILES.get("pic") print(file_object.name) f=open('a1.jpg',mode='wb') for chunk in file_object.chunks(): f.write(chunk) f.close() return render(request,'function.html') def index(request): return render(request,'index.html') def f1_b(request): img = cv2.imread('./a1.jpg') b = img[:, :, 0] g = img[:, :, 1] r = img[:, :, 2] cv2.imwrite('./static/abc.jpg', b) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f1_g(request): img = cv2.imread('./a1.jpg') b = img[:, :, 0] g = img[:, :, 1] r = img[:, :, 2] cv2.imwrite('./static/abc.jpg', g) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f1_r(request): img = cv2.imread('./a1.jpg') b = img[:, :, 0] g = img[:, :, 1] r = img[:, :, 2] cv2.imwrite('./static/abc.jpg', r) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f2_h(request): img = cv2.imread('./a1.jpg') hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h = hsv[:, :, 0] s = hsv[:, :, 1] v = hsv[:, :, 2] cv2.imwrite('./static/abc.jpg', h) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f2_s(request): img = cv2.imread('./a1.jpg') hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h = hsv[:, :, 0] s = hsv[:, :, 1] v = hsv[:, :, 2] cv2.imwrite('./static/abc.jpg', s) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f2_v(request): img = cv2.imread('./a1.jpg') hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) h = hsv[:, :, 0] s = hsv[:, :, 1] v = hsv[:, :, 2] cv2.imwrite('./static/abc.jpg', v) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f3(request): img = cv2.imread('./a1.jpg') # l, w, h = img.shape # 放大图像至原来的两倍，使用双线性插值法 img = cv2.resize(img, (0, 0), fx=2, fy=2, interpolation=cv2.INTER_LINEAR) height, width, channel = img.shape # 构建移动矩阵,x轴左移 30 个像素，y轴下移 60 个像素 M = np.float32([[1, 0, 30], [0, 1, 60]]) img = cv2.warpAffine(img, M, (width, height)) # 构建矩阵，旋转中心坐标为处理后图片长宽的一半，旋转角度为45度，缩放因子为1 M = cv2.getRotationMatrix2D((width / 2, height / 2), 45, 1) dst = cv2.warpAffine(img, M, (width, height)) cv2.imwrite('./static/abc.jpg', dst) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f4_1(request): img = cv2.imread('./a1.jpg') src = cv2.resize(img, (256, 256)) # 水平镜像 horizontal = cv2.flip(src, 1, dst=None) # 垂直镜像 vertical = cv2.flip(src, 0, dst=None) # 对角镜像 ，并保存 cross = cv2.flip(src, -1, dst=None) cv2.imwrite('./static/abc.jpg', horizontal) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f4_2(request): img = cv2.imread('./a1.jpg') src = cv2.resize(img, (256, 256)) # 水平镜像 horizontal = cv2.flip(src, 1, dst=None) # 垂直镜像 vertical = cv2.flip(src, 0, dst=None) # 对角镜像 ，并保存 cross = cv2.flip(src, -1, dst=None) cv2.imwrite('./static/abc.jpg', vertical) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f4_3(request): img = cv2.imread('./a1.jpg') src = cv2.resize(img, (256, 256)) # 水平镜像 horizontal = cv2.flip(src, 1, dst=None) # 垂直镜像 vertical = cv2.flip(src, 0, dst=None) # 对角镜像 ，并保存 cross = cv2.flip(src, -1, dst=None) cv2.imwrite('./static/abc.jpg', cross) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f5(request): img = cv2.imread('./a1.jpg') src = cv2.resize(img, (256, 256)) # 获取图像shape rows, cols = src.shape[: 2] # 设置图像仿射变化矩阵 post1 = np.float32([[50, 50], [200, 50], [50, 200]]) post2 = np.float32([[10, 100], [200, 50], [100, 250]]) M = cv2.getAffineTransform(post1, post2) # 图像仿射变换，及保存 result = cv2.warpAffine(src, M, (rows, cols)) cv2.imwrite('./static/abc.jpg', result) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f6(request): def histCover(img, fileName): plt.figure(fileName, figsize=(16, 8)) # 展示输入图像 plt.subplot(121) plt.imshow(img, "gray") # 展示直方图 plt.subplot(122) """ 利用cv.calcHist()内置函数进行画灰度图像直方图，该函数的返回值是hist """ hist = cv2.calcHist([img], [0], None, [256], [0, 255]) plt.plot(hist) plt.xlim([0, 255]) plt.savefig(fileName) plt.show() # 主函数的定义，定义图片路径 def main_func(argv): img_plt = './static/abc.jpg' """ 读入图像，并转化为灰度值，数据路径为img_path """ img_gray = cv2.imread('./a1.jpg', cv2.IMREAD_GRAYSCALE) # img= cv.imread(img_path) # img_gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY) """ 调用histCover函数绘制直方图，看清楚该函数是无返回值的哦 """ histCover(img_gray, img_plt) main_func(sys.argv) return render(request,'index.html',{'image':'/static/abc.jpg'}) def f7(request): def histCover(img, fileName): color = ["r", "g", "b"] # 展示原始图像 """ 展示原始图像,因为用到cv2函数读取，要用matplotlib库函数，所以应该转BGR格式为RGB格式 """ b, g, r = cv2.split(img) img = cv2.merge([r, g, b]) plt.subplot(121) plt.imshow(img) plt.subplot(122) # 绘制彩色直方图，需要对每个通道进行遍历，并且找到最大值和最小值 for index, c in enumerate(color): """ 对每个通道进行直方图的计算和绘制，需要调用cv2的计算直方图函数，返回值为hist """ hist = cv2.calcHist([img], [index], None, [256], [0, 255]) plt.plot(hist, color=c) plt.xlim([0, 255]) plt.savefig(fileName) plt.show() # 主函数的定义，定义图片路径 def main_func(argv): img_path = './a1.jpg' img_plt = './static/abc.jpg' """ 根据给出的图像路径，加载图像，图像数据路径为img_path，返回值为imgOri1 """ imgOri1 = cv2.imread(img_path) histCover(imgOri1, img_plt) main_func(sys.argv) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f8(request): img = cv2.imread('./a1.jpg') # 根据路径读取一张图片 color = ('b', 'g', 'r') for i, col in enumerate(color): histr = cv2.calcHist([img], [i], None, [256], [0, 256]) plt.plot(histr, color=col) plt.xlim([0, 256]) plt.savefig('./static/abc.jpg') return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f9(request): def grayHist(img, filename): plt.figure(filename, figsize=(16, 8)) # 展示输入图像 plt.subplot(121) plt.imshow(img, 'gray') # 展示直方图 plt.subplot(122) h, w = img.shape[:2] """ 任务1.将二维图像矩阵reshape()为一维数组，返回值为pixelSequence """ pixelSequence = img.reshape(h * w, 1) # 将二维图像矩阵reshape()为一维数组 numberBins = 256 """ 调用hist()的方法进行直方图的绘制，返回值有histogram, bins, patch """ histogram, bins, patch = plt.hist(pixelSequence, numberBins) print(max(histogram)) plt.xlabel("gray label") plt.ylabel("number of pixels") plt.axis([0, 255, 0, np.max(histogram)]) # 打印输出峰值 plt.savefig(filename) plt.show() # 定义图像数据的路径 img_path = './a1.jpg' #out_path = 'D:/Mike/PycharmProjects/pythonProject/venv/abc.jpg' out2_path = './static/abc.jpg' img = cv2.imread(img_path, 0) h, w = img.shape[:2] out = np.zeros(img.shape, np.uint8) """ 通过遍历对不同像素范围内进行分段线性变化，在这里分三段函数进行分段线性变化,主要还是考察for循环的应用 #y=0.5*x(x<50) #y=3.6*x-310(50<=x<150) #y=0.238*x+194(x>=150) """ for i in range(h): for j in range(w): pix = img[i][j] if pix < 50: out[i][j] = 0.5 * pix elif pix < 150: out[i][j] = 3.6 * pix - 310 else: out[i][j] = 0.238 * pix + 194 out = np.around(out) out = out.astype(np.uint8) #grayHist(img, out_path) grayHist(out, out2_path) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f10(request): img = cv2.imread('./a1.jpg') # 根据路径读取一张图片 img = cv2.GaussianBlur(img, (3, 3), 0) edges = cv2.Canny(img, 50, 150, apertureSize=3) # 使用HoughLines算法 # rho为1 # theta为np.pi/2 # threshold为 118 # 其他的默认 lines = cv2.HoughLines(edges, 1, np.pi / 2, 118) result = img.copy() for i_line in lines: for line in i_line: rho = line[0] theta = line[1] if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线 pt1 = (int(rho / np.cos(theta)), 0) pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0]) cv2.line(result, pt1, pt2, (0, 0, 255)) else: pt1 = (0, int(rho / np.sin(theta))) pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta))) cv2.line(result, pt1, pt2, (0, 0, 255), 1) minLineLength = 200 maxLineGap = 15 # 使用HoughLinesP算法 # rho为1 # theta为np.pi/2 # threshold为 118 # 并设置上面提供的minLineLength和maxLineGap # 其他的默认 linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 80, minLineLength, maxLineGap) result_P = img.copy() for i_P in linesP: for x1, y1, x2, y2 in i_P: cv2.line(result_P, (x1, y1), (x2, y2), (0, 255, 0), 3) cv2.imwrite('./static/abc.jpg', result) #cv2.imwrite('D:/Mike/PycharmProjects/pythonProject/venv/def.jpg', result_P) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f11(request): img = cv2.imread('./a1.jpg') # 根据路径读取一张图片 img = cv2.GaussianBlur(img, (3, 3), 0) edges = cv2.Canny(img, 50, 150, apertureSize=3) # 使用HoughLines算法 # rho为1 # theta为np.pi/2 # threshold为 118 # 其他的默认 lines = cv2.HoughLines(edges, 1, np.pi / 2, 118) result = img.copy() for i_line in lines: for line in i_line: rho = line[0] theta = line[1] if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线 pt1 = (int(rho / np.cos(theta)), 0) pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0]) cv2.line(result, pt1, pt2, (0, 0, 255)) else: pt1 = (0, int(rho / np.sin(theta))) pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta))) cv2.line(result, pt1, pt2, (0, 0, 255), 1) minLineLength = 200 maxLineGap = 15 # 使用HoughLinesP算法 # rho为1 # theta为np.pi/2 # threshold为 118 # 并设置上面提供的minLineLength和maxLineGap # 其他的默认 linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 80, minLineLength, maxLineGap) result_P = img.copy() for i_P in linesP: for x1, y1, x2, y2 in i_P: cv2.line(result_P, (x1, y1), (x2, y2), (0, 255, 0), 3) #cv2.imwrite('D:/Mike/PycharmProjects/djangoProject/static/abc.jpg', result) cv2.imwrite('./static/abc.jpg', result_P) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f12(request): filename = './a1.jpg' # 1. 灰度模式读取图像，图像名为CRH CRH = cv2.imread(filename, 0) # 2. 计算图像梯度。首先要对读取的图像进行数据变换，因为使用了 # numpy对梯度进行数值计算，所以要使用 # CRH.astype('float')进行数据格式变换。 row, column = CRH.shape CRH_f = np.copy(CRH) gradient = np.zeros((row, column)) CRH = CRH.astype('float') for x in range(row - 1): for y in range(column - 1): gx = abs(CRH[x + 1, y] - CRH[x, y]) gy = abs(CRH[x, y + 1] - CRH[x, y]) gradient[x, y] = gx + gy # 3. 对图像进行增强，增强后的图像变量名为sharp sharp = CRH_f + gradient sharp = np.where(sharp > 255, 255, sharp) sharp = np.where(sharp < 0, 0, sharp) # 数据类型变换 gradient = gradient.astype('uint8') sharp = sharp.astype('uint8') # 保存图像 filepath = './static/abc.jpg' cv2.imwrite(filepath, gradient) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f13_op(request): img = cv2.imread('./a1.jpg', cv2.IMREAD_UNCHANGED) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) # 2. 定义十字形结构元素 kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10)) # 3. 对二值图进行开运算和闭运算操作 im_op = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) #im_cl = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel) cv2.imwrite('./static/abc.jpg', im_op) #cv2.imwrite('D:/Mike/PycharmProjects/pythonProject/venv/def.jpg', im_cl) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f13_cl(request): img = cv2.imread('./a1.jpg', cv2.IMREAD_UNCHANGED) img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY) # 2. 定义十字形结构元素 kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10)) # 3. 对二值图进行开运算和闭运算操作 #im_op = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel) im_cl = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel) cv2.imwrite('./static/abc.jpg', im_cl) #cv2.imwrite('D:/Mike/PycharmProjects/pythonProject/venv/def.jpg', im_cl) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f14(request): filename = './a1.jpg' # 读取图像 image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE) # 待输出的图片 output = np.zeros(image.shape, np.uint8) # 遍历图像，获取叠加噪声后的图像 for i in range(image.shape[0]): for j in range(image.shape[1]): if image[i][j] < 100: # 添加食盐噪声 output[i][j] = 255 # 添加胡椒噪声 elif image[i][j] > 200: output[i][j] = 0 # 不添加噪声 else: output[i][j] = image[i][j] cv2.imwrite('./static/abc.jpg', output) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f15(request): filename = './a1.jpg' # 读取图像 image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE) # 待输出的图片 output = np.zeros(image.shape, np.uint8) # 遍历图像，进行均值滤波 for i in range(image.shape[0]): for j in range(image.shape[1]): # 计算均值,完成对图片src的几何均值滤波 ji = 1.0 # 遍历滤波器内的像素值 for m in range(-1, 2): # for n in range(-1, 2): # 防止越界 if 0 <= j + m < image.shape[1]: ji *= image[i][j + m] # 滤波器的大小为1*3 output[i][j] = pow(ji, 1 / 3) cv2.imwrite('./static/abc.jpg', output) return render(request, 'index.html', {'image': '/static/abc.jpg'}) def f16(request): return render(request,'home1.html') def f17(request): file_object = request.FILES.get("load") print(file_object.name) f = open('a2.png', mode='wb') for chunk in file_object.chunks(): f.write(chunk) f.close() def color_transfer(source, target): source = cv2.cvtColor(source, cv2.COLOR_BGR2LAB).astype("float32") target = cv2.cvtColor(target, cv2.COLOR_BGR2LAB).astype("float32") (lMeanSrc, lStdSrc, aMeanSrc, aStdSrc, bMeanSrc, bStdSrc) = image_stats(source) (lMeanTar, lStdTar, aMeanTar, aStdTar, bMeanTar, bStdTar) = image_stats(target) (l, a, b) = cv2.split(target) l -= lMeanTar a -= aMeanTar b -= bMeanTar l = (lStdTar / lStdSrc) * l a = (aStdTar / aStdSrc) * a b = (bStdTar / bStdSrc) * b l += lMeanSrc a += aMeanSrc b += bMeanSrc l = np.clip(l, 0, 255) a = np.clip(a, 0, 255) b = np.clip(b, 0, 255) transfer = cv2.merge([l, a, b]) transfer = cv2.cvtColor(transfer.astype("uint8"), cv2.COLOR_LAB2BGR) return transfer def image_stats(image): (l, a, b) = cv2.split(image) (lMean, lStd) = (l.mean(), l.std()) (aMean, aStd) = (a.mean(), a.std()) (bMean, bStd) = (b.mean(), b.std()) return (lMean, lStd, aMean, aStd, bMean, bStd) source = cv2.imread('./a2.png') target = cv2.imread('./a1.jpg') taransform = color_transfer(source, target) source1 = cv2.resize(source, target.shape[0:2]) target1 = cv2.resize(target, target.shape[0:2]) taransform1 = cv2.resize(taransform, target.shape[0:2]) #result = np.hstack([source1, target1, taransform1]) result = np.hstack([taransform1]) cv2.imwrite('./static/abc.jpg', result) return render(request, 'index.html', {'image': '/static/abc.jpg'})