diff --git a/djangoProject b/djangoProject
deleted file mode 100644
index 41d7671..0000000
--- a/djangoProject
+++ /dev/null
@@ -1,176 +0,0 @@
-import cv2
-import numpy as np
-import math
-
-
-# RGB色彩空间
-def Rgb(path):
-    img = cv2.imread(path, 1)
-    b = img[:, :, 0]
-    g = img[:, :, 1]
-    r = img[:, :, 2]
-    cv2.imwrite('./media/RGB/b.jpg', b)
-    cv2.imwrite('./media/RGB/g.jpg', g)
-    cv2.imwrite('./media/RGB/r.jpg', r)
-
-
-# HSV色彩空间
-def Hsv(path):
-    img = cv2.imread(path, 1)
-    hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
-    h = hsv[:, :, 0]
-    s = hsv[:, :, 1]
-    v = hsv[:, :, 2]
-    cv2.imwrite("./media/HSV/h.jpg", h)
-    cv2.imwrite("./media/HSV/s.jpg", s)
-    cv2.imwrite("./media/HSV/v.jpg", v)
-
-
-def Un(path):
-    img = cv2.imread(path, 0)
-    result = ~img
-    cv2.imwrite("./media/UN/un.jpg", result)
-
-
-def Add(paths):
-    # 只做了两个图像叠加，多个怎么做
-    img1 = cv2.imread(paths[0], 1)
-    img2 = cv2.imread(paths[1], 1)
-    img = cv2.add(img1, img2)
-    cv2.imwrite("./media/ADD/add.jpg", img)
-
-
-def Subtract(paths):
-    img1 = cv2.imread(paths[0], 1)
-    img2 = cv2.imread(paths[1], 1)
-    img = cv2.subtract(img1, img2)
-    cv2.imwrite("./media/SUBTRACT/subtract.jpg", img)
-
-
-# 形态学操作
-def OpenAndClose(path):
-    # 二值转换
-    img = cv2.imread(path, 1)
-    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
-    retval, src = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
-    # 定义十字形结构元素
-    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
-    # 对二值图进行开运算和闭运算操作
-    im_op = cv2.morphologyEx(src, cv2.MORPH_OPEN, kernel)
-    im_cl = cv2.morphologyEx(src, cv2.MORPH_CLOSE, kernel)
-    cv2.imwrite('./media/OPANDCL/op.png', im_op)
-    cv2.imwrite('./media/OPANDCL/cl.png', im_cl)
-
-
-# 边缘检测和图像增强
-def Sharp(path):
-    CRH = cv2.imread(path, 1)
-    gradient = np.zeros_like(CRH)
-    CRH = CRH.astype('float')
-    h, w = CRH.shape[:2]
-    for x in range(h - 1):
-        for y in range(w - 1):
-            gx = abs(CRH[x + 1, y] - CRH[x, y])
-            gy = abs(CRH[x, y + 1] - CRH[x, y])
-            gradient[x, y] = gx + gy
-    sharp = CRH + gradient
-
-    sharp = np.where(sharp > 255, 255, sharp)
-    sharp = np.where(sharp < 0, 0, sharp)
-    gradient = gradient.astype('uint8')
-    sharp = sharp.astype('uint8')
-    cv2.imwrite('./media/SHARP/gradient.png', gradient)
-    cv2.imwrite('./media/SHARP/sharp.png', sharp)
-
-
-# 镜像
-def Turn(path):
-    img = cv2.imread(path)
-    # 水平镜像
-    horizontal = cv2.flip(img, 1, dst=None)
-    # 垂直镜像
-    vertical = cv2.flip(img, 0, dst=None)
-    # 对角镜像
-    cross = cv2.flip(img, -1, dst=None)
-    cv2.imwrite("./media/TURN/horizontal.jpg", horizontal)
-    cv2.imwrite("./media/TURN/vertical.jpg", vertical)
-    cv2.imwrite("./media/TURN/cross.jpg", cross)
-
-
-# 腐蚀
-def Erosion(path):
-    src = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-    # 10x10的交叉型结构元
-    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
-    erosion = cv2.erode(src, kernel)
-    cv2.imwrite("./media/EROSION/erosion.jpg", erosion)
-
-
-# 膨胀
-def Dilation(path):
-    src = cv2.imread(path, cv2.IMREAD_UNCHANGED)
-    # 10x10的交叉结构元
-    kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (10, 10), (-1, -1))
-    dilation = cv2.dilate(src, kernel)
-    cv2.imwrite("./media/DILATION/dilation.png", dilation)
-
-
-# 噪声及滤波
-def Noise(path):
-    image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
-    output = np.zeros(image.shape, np.uint8)
-    output2 = np.zeros(image.shape, np.uint8)
-    # 叠加噪声
-    for i in range(image.shape[0]):
-        for j in range(image.shape[1]):
-            if image[i][j] < 40:
-                # 添加食盐噪声
-                output[i][j] = 255
-            elif image[i][j] > 200:
-                # 添加胡椒噪声
-                output[i][j] = 0
-                # 不添加噪声
-            else:
-                output[i][j] = image[i][j]
-
-    # 均值滤波
-    for i in range(output.shape[0]):
-        for j in range(output.shape[1]):
-            ji = 1.0
-            for n in range(-1, 2):
-                if 0 <= i < output.shape[0] and 0 <= j + n < output.shape[1]:
-                    ji = ji * output[i][j + n]
-            output2[i][j] = int(ji ** (1 / 3))
-
-    cv2.imwrite("./media/NOISE/noise.jpg", output)
-    cv2.imwrite("./media/NOISE/meanFilter.jpg", output2)
-
-# # 扩展平移缩放
-# def moveandchange(path):
-#     img = cv2.imread(path, 1)
-#     l, w, h = img.shape
-#     # 扩展，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
-#     # 移动
-#     M = np.float32([[1, 0, 30], [0, 1, 60]])
-#     img = cv2.warpAffine(img, M, (width, height))
-#     # 旋转
-#     rows, cols, depth = img.shape
-#     M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 45, 1)
-#     dst = cv2.warpAffine(img, M, (width, height))
-#
-#     cv2.imwrite('./media/CHANGE/out.png', dst)
-
-
-# # 仿射变换
-# def affine(path):
-#     img = cv2.imread(path)
-#     rows, cols = img.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
-#     result = cv2.warpAffine(img, M, (rows, cols))
-#     cv2.imwrite("./media/AFFINE/affine.jpg", result)