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from django.shortcuts import render,HttpResponse
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import numpy as np
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from io import BytesIO
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import cv2
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import sys
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import matplotlib.pyplot as plt
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import numpy
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# Create your views here.
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def home(request):
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return render(request,'home.html') #render用来打开html
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def login(request):
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if request.method=='GET':
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return render(request,'login.html')
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else:
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uname=request.POST.get('username')
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pwd = request.POST.get('password')
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if uname=='user' and pwd=='password':
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return render(request, 'home.html')
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else:
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return HttpResponse('用户名或密码错误！')
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'''def login2(request):
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return render(request,'home.html')'''
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def photo(request):
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file_object=request.FILES.get("pic")
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print(file_object.name)
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f=open('a1.jpg',mode='wb')
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for chunk in file_object.chunks():
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f.write(chunk)
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f.close()
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return render(request,'function.html')
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def index(request):
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return render(request,'index.html')
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def f1_b(request):
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img = cv2.imread('./a1.jpg')
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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('./static/abc.jpg', b)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f1_g(request):
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img = cv2.imread('./a1.jpg')
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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('./static/abc.jpg', g)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f1_r(request):
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img = cv2.imread('./a1.jpg')
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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('./static/abc.jpg', r)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f2_h(request):
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img = cv2.imread('./a1.jpg')
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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('./static/abc.jpg', h)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f2_s(request):
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img = cv2.imread('./a1.jpg')
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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('./static/abc.jpg', s)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f2_v(request):
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img = cv2.imread('./a1.jpg')
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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('./static/abc.jpg', v)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f3(request):
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img = cv2.imread('./a1.jpg')
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# l, w, h = img.shape
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# 放大图像至原来的两倍，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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# 构建移动矩阵,x轴左移 30 个像素，y轴下移 60 个像素
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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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# 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
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M = cv2.getRotationMatrix2D((width / 2, height / 2), 45, 1)
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dst = cv2.warpAffine(img, M, (width, height))
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cv2.imwrite('./static/abc.jpg', dst)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f4_1(request):
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img = cv2.imread('./a1.jpg')
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src = cv2.resize(img, (256, 256))
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# 水平镜像
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horizontal = cv2.flip(src, 1, dst=None)
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# 垂直镜像
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vertical = cv2.flip(src, 0, dst=None)
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# 对角镜像 ，并保存
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cross = cv2.flip(src, -1, dst=None)
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cv2.imwrite('./static/abc.jpg', horizontal)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f4_2(request):
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img = cv2.imread('./a1.jpg')
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src = cv2.resize(img, (256, 256))
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# 水平镜像
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horizontal = cv2.flip(src, 1, dst=None)
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# 垂直镜像
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vertical = cv2.flip(src, 0, dst=None)
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# 对角镜像 ，并保存
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cross = cv2.flip(src, -1, dst=None)
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cv2.imwrite('./static/abc.jpg', vertical)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f4_3(request):
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img = cv2.imread('./a1.jpg')
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src = cv2.resize(img, (256, 256))
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# 水平镜像
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horizontal = cv2.flip(src, 1, dst=None)
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# 垂直镜像
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vertical = cv2.flip(src, 0, dst=None)
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# 对角镜像 ，并保存
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cross = cv2.flip(src, -1, dst=None)
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cv2.imwrite('./static/abc.jpg', cross)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f5(request):
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img = cv2.imread('./a1.jpg')
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src = cv2.resize(img, (256, 256))
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# 获取图像shape
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rows, cols = src.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，及保存
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result = cv2.warpAffine(src, M, (rows, cols))
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cv2.imwrite('./static/abc.jpg', result)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f6(request):
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def histCover(img, fileName):
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plt.figure(fileName, figsize=(16, 8))
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# 展示输入图像
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plt.subplot(121)
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plt.imshow(img, "gray")
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# 展示直方图
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plt.subplot(122)
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"""
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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
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"""
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hist = cv2.calcHist([img], [0], None, [256], [0, 255])
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plt.plot(hist)
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plt.xlim([0, 255])
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plt.savefig(fileName)
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plt.show()
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# 主函数的定义，定义图片路径
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def main_func(argv):
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img_plt = './static/abc.jpg'
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"""
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读入图像，并转化为灰度值，数据路径为img_path
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"""
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img_gray = cv2.imread('./a1.jpg', cv2.IMREAD_GRAYSCALE)
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# img= cv.imread(img_path)
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# img_gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)
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"""
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调用histCover函数绘制直方图，看清楚该函数是无返回值的哦
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"""
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histCover(img_gray, img_plt)
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main_func(sys.argv)
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return render(request,'index.html',{'image':'/static/abc.jpg'})
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def f7(request):
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def histCover(img, fileName):
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color = ["r", "g", "b"]
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# 展示原始图像
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"""
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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
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"""
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b, g, r = cv2.split(img)
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img = cv2.merge([r, g, b])
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plt.subplot(121)
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plt.imshow(img)
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plt.subplot(122)
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# 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
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for index, c in enumerate(color):
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"""
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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
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"""
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hist = cv2.calcHist([img], [index], None, [256], [0, 255])
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plt.plot(hist, color=c)
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plt.xlim([0, 255])
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plt.savefig(fileName)
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plt.show()
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# 主函数的定义，定义图片路径
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def main_func(argv):
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img_path = './a1.jpg'
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img_plt = './static/abc.jpg'
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"""
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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
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"""
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imgOri1 = cv2.imread(img_path)
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histCover(imgOri1, img_plt)
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main_func(sys.argv)
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return render(request, 'index.html', {'image': '/static/abc.jpg'})
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def f8(request):
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img = cv2.imread('./a1.jpg') # 根据路径读取一张图片
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color = ('b', 'g', 'r')
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for i, col in enumerate(color):
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histr = cv2.calcHist([img], [i], None, [256], [0, 256])
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plt.plot(histr, color=col)
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plt.xlim([0, 256])
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plt.savefig('./static/abc.jpg')
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return render(request, 'index.html', {'image': '/static/abc.jpg'})
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def f9(request):
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def grayHist(img, filename):
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plt.figure(filename, figsize=(16, 8))
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# 展示输入图像
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plt.subplot(121)
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plt.imshow(img, 'gray')
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# 展示直方图
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plt.subplot(122)
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h, w = img.shape[:2]
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"""
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任务1.将二维图像矩阵reshape()为一维数组，返回值为pixelSequence
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"""
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pixelSequence = img.reshape(h * w, 1)
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# 将二维图像矩阵reshape()为一维数组
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numberBins = 256
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"""
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调用hist()的方法进行直方图的绘制，返回值有histogram, bins, patch
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"""
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histogram, bins, patch = plt.hist(pixelSequence, numberBins)
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print(max(histogram))
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plt.xlabel("gray label")
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plt.ylabel("number of pixels")
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plt.axis([0, 255, 0, np.max(histogram)])
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# 打印输出峰值
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plt.savefig(filename)
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plt.show()
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# 定义图像数据的路径
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img_path = './a1.jpg'
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#out_path = 'D:/Mike/PycharmProjects/pythonProject/venv/abc.jpg'
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out2_path = './static/abc.jpg'
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img = cv2.imread(img_path, 0)
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h, w = img.shape[:2]
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out = np.zeros(img.shape, np.uint8)
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"""
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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
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#y=0.5*x(x<50)
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#y=3.6*x-310(50<=x<150)
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#y=0.238*x+194(x>=150)
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"""
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for i in range(h):
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for j in range(w):
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pix = img[i][j]
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if pix < 50:
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out[i][j] = 0.5 * pix
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elif pix < 150:
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out[i][j] = 3.6 * pix - 310
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else:
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out[i][j] = 0.238 * pix + 194
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out = np.around(out)
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out = out.astype(np.uint8)
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#grayHist(img, out_path)
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grayHist(out, out2_path)
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return render(request, 'index.html', {'image': '/static/abc.jpg'})
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def f10(request):
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img = cv2.imread('./a1.jpg') # 根据路径读取一张图片
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img = cv2.GaussianBlur(img, (3, 3), 0)
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edges = cv2.Canny(img, 50, 150, apertureSize=3)
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# 使用HoughLines算法
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# rho为1
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# theta为np.pi/2
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# threshold为 118
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# 其他的默认
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lines = cv2.HoughLines(edges, 1, np.pi / 2, 118)
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result = img.copy()
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for i_line in lines:
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for line in i_line:
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rho = line[0]
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theta = line[1]
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if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线
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pt1 = (int(rho / np.cos(theta)), 0)
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pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0])
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cv2.line(result, pt1, pt2, (0, 0, 255))
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else:
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pt1 = (0, int(rho / np.sin(theta)))
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pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta)))
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cv2.line(result, pt1, pt2, (0, 0, 255), 1)
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minLineLength = 200
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maxLineGap = 15
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# 使用HoughLinesP算法
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# rho为1
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# theta为np.pi/2
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# threshold为 118
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# 并设置上面提供的minLineLength和maxLineGap
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# 其他的默认
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linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 80, minLineLength, maxLineGap)
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result_P = img.copy()
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for i_P in linesP:
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for x1, y1, x2, y2 in i_P:
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cv2.line(result_P, (x1, y1), (x2, y2), (0, 255, 0), 3)
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cv2.imwrite('./static/abc.jpg', result)
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#cv2.imwrite('D:/Mike/PycharmProjects/pythonProject/venv/def.jpg', result_P)
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return render(request, 'index.html', {'image': '/static/abc.jpg'})
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def f11(request):
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img = cv2.imread('./a1.jpg') # 根据路径读取一张图片
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img = cv2.GaussianBlur(img, (3, 3), 0)
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edges = cv2.Canny(img, 50, 150, apertureSize=3)
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# 使用HoughLines算法
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# rho为1
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# theta为np.pi/2
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# threshold为 118
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# 其他的默认
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lines = cv2.HoughLines(edges, 1, np.pi / 2, 118)
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result = img.copy()
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for i_line in lines:
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for line in i_line:
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rho = line[0]
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theta = line[1]
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if (theta < (np.pi / 4.)) or (theta > (3. * np.pi / 4.0)): # 垂直直线
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pt1 = (int(rho / np.cos(theta)), 0)
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pt2 = (int((rho - result.shape[0] * np.sin(theta)) / np.cos(theta)), result.shape[0])
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cv2.line(result, pt1, pt2, (0, 0, 255))
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else:
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pt1 = (0, int(rho / np.sin(theta)))
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pt2 = (result.shape[1], int((rho - result.shape[1] * np.cos(theta)) / np.sin(theta)))
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cv2.line(result, pt1, pt2, (0, 0, 255), 1)
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minLineLength = 200
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maxLineGap = 15
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# 使用HoughLinesP算法
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# rho为1
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# theta为np.pi/2
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# threshold为 118
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# 并设置上面提供的minLineLength和maxLineGap
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# 其他的默认
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linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 80, minLineLength, maxLineGap)
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result_P = img.copy()
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for i_P in linesP:
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for x1, y1, x2, y2 in i_P:
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cv2.line(result_P, (x1, y1), (x2, y2), (0, 255, 0), 3)
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#cv2.imwrite('D:/Mike/PycharmProjects/djangoProject/static/abc.jpg', result)
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cv2.imwrite('./static/abc.jpg', result_P)
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return render(request, 'index.html', {'image': '/static/abc.jpg'})
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def f12(request):
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filename = './a1.jpg'
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# 1. 灰度模式读取图像，图像名为CRH
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CRH = cv2.imread(filename, 0)
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# 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
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# numpy对梯度进行数值计算，所以要使用
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# CRH.astype('float')进行数据格式变换。
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row, column = CRH.shape
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CRH_f = np.copy(CRH)
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gradient = np.zeros((row, column))
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CRH = CRH.astype('float')
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for x in range(row - 1):
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for y in range(column - 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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# 3. 对图像进行增强，增强后的图像变量名为sharp
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sharp = CRH_f + 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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# 数据类型变换
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gradient = gradient.astype('uint8')
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sharp = sharp.astype('uint8')
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# 保存图像
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filepath = './static/abc.jpg'
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cv2.imwrite(filepath, gradient)
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return render(request, 'index.html', {'image': '/static/abc.jpg'})
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def f13_op(request):
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img = cv2.imread('./a1.jpg', cv2.IMREAD_UNCHANGED)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
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# 2. 定义十字形结构元素
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kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10))
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# 3. 对二值图进行开运算和闭运算操作
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im_op = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
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#im_cl = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
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cv2.imwrite('./static/abc.jpg', im_op)
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#cv2.imwrite('D:/Mike/PycharmProjects/pythonProject/venv/def.jpg', im_cl)
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return render(request, 'index.html', {'image': '/static/abc.jpg'})
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def f13_cl(request):
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img = cv2.imread('./a1.jpg', cv2.IMREAD_UNCHANGED)
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img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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ret, img = cv2.threshold(img, 127, 255, cv2.THRESH_BINARY)
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# 2. 定义十字形结构元素
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kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10))
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# 3. 对二值图进行开运算和闭运算操作
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#im_op = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
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im_cl = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
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cv2.imwrite('./static/abc.jpg', im_cl)
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#cv2.imwrite('D:/Mike/PycharmProjects/pythonProject/venv/def.jpg', im_cl)
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return render(request, 'index.html', {'image': '/static/abc.jpg'})
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def f14(request):
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filename = './a1.jpg'
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# 读取图像
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image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
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# 待输出的图片
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output = np.zeros(image.shape, np.uint8)
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# 遍历图像，获取叠加噪声后的图像
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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] < 100:
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# 添加食盐噪声
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output[i][j] = 255
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# 添加胡椒噪声
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elif image[i][j] > 200:
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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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cv2.imwrite('./static/abc.jpg', output)
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|
return render(request, 'index.html', {'image': '/static/abc.jpg'})
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|
|
def f15(request):
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|
filename = './a1.jpg'
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|
# 读取图像
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|
image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
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|
# 待输出的图片
|
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|
output = np.zeros(image.shape, np.uint8)
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|
# 遍历图像，进行均值滤波
|
|
|
for i in range(image.shape[0]):
|
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|
for j in range(image.shape[1]):
|
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|
# 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'})
|
