diff --git a/djangoProject b/djangoProject
new file mode 100644
index 0000000..41d7671
--- /dev/null
+++ b/djangoProject
@@ -0,0 +1,176 @@
+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)