import json
import math
import random
import uuid

import cv2
import matplotlib.pyplot as plt
import numpy as np
from django.http import HttpResponse
from django.shortcuts import render
# Create your views here.
from django.views.decorators.csrf import csrf_exempt

PREFIX = 'media/'

DEFAULT_FORMAT = '.jpg'

SUPPORT_FILE_FORMAT = ['png', 'jpg', 'bmp', 'jpeg', 'jpe', 'dib', 'pbm', 'pgm', 'ppm', 'tiff', 'tif']


# 获取uuid图片名
def getImageName():
    return uuid.uuid1().__str__()


# 保存图片，并判断是否为合适的扩展名
def getImage(request):
    print(request.FILES)
    file = request.FILES.get('image')
    suffix = file.name[file.name.rfind('.') + 1:].lower()
    if suffix not in SUPPORT_FILE_FORMAT:
        return ''
    filename = getImageName() + '.' + suffix
    img = open(PREFIX + filename, 'wb+')
    for chunk in file.chunks():
        img.write(chunk)
    img.close()
    return filename


# 接收图片，转发到getImage
@csrf_exempt
def upload(request):
    imageName = getImage(request)
    if imageName == '':
        return HttpResponse('Not supported image format')
    return HttpResponse(imageName)


def process_bg():
    img = np.zeros((800, 800, 3), np.uint8)
    filename = getImageName() + DEFAULT_FORMAT
    cv2.imwrite(PREFIX + filename, img)
    return filename


def process_line(para):
    name, start, end, color, thickness = para['img'], para['start'], para['end'], para['color'], para['thickness']
    img = cv2.imread(PREFIX + name)
    cv2.line(img, tuple(start), tuple(end), tuple(color), thickness)
    cv2.imwrite(PREFIX + name, img)
    return name


def process_rectangle(para):
    name, start, end, color, thickness = para['img'], para['start'], para['end'], para['color'], para['thickness']
    img = cv2.imread(PREFIX + name)
    cv2.rectangle(img, tuple(start), tuple(end), tuple(color), thickness)
    cv2.imwrite(PREFIX + name, img)
    return name


def process_circle(para):
    name, center, radius, color, thickness = para['img'], para['center'], para['radius'], para['color'], para[
        'thickness']
    img = cv2.imread(PREFIX + name)
    cv2.circle(img, tuple(center), radius, tuple(color), thickness)
    cv2.imwrite(PREFIX + name, img)
    return name


def process_ellipse(para):
    name, center, axes, angle, startangle, endangle, color, thickness = para['img'], para['center'], para['axes'], para[
        'angle'], para['startangle'], para['endangle'], para['color'], para['thickness']
    img = cv2.imread(PREFIX + name)
    cv2.ellipse(img, tuple(center), tuple(axes), angle, startangle, endangle, tuple(color), thickness)
    cv2.imwrite(PREFIX + name, img)
    return name


def process_polylines(para):
    name, point, isclosed, color = para['img'], para['point'], para['isclosed'], para['color']
    img = cv2.imread(PREFIX + name)
    pts = np.array(point, np.int32)
    pts = pts.reshape((-1, 1, 2))
    cv2.polylines(img, [pts], isclosed, tuple(color))
    cv2.imwrite(PREFIX + name, img)
    return name


def process_text(para):
    name, text, org, fontsize, color = para['img'], para['text'], para['org'], para['fontsize'], para['color']
    img = cv2.imread(PREFIX + name)
    cv2.putText(img, text, org, fontFace=cv2.FONT_HERSHEY_SCRIPT_COMPLEX, fontScale=fontsize, color=tuple(color))
    cv2.imwrite(PREFIX + name, img)
    return name


# 使用opencv 基本绘图
@csrf_exempt
def basic_drawing(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        draw_type = para['type']
        if draw_type == 'bg':
            return HttpResponse(process_bg())
        else:
            if draw_type == 'line':
                return HttpResponse(process_line(para))
            elif draw_type == 'rectangle':
                return HttpResponse(process_rectangle(para))
            elif draw_type == 'circle':
                return HttpResponse(process_circle(para))
            elif draw_type == 'ellipse':
                return HttpResponse(process_ellipse(para))
            elif draw_type == 'polylines':
                return HttpResponse(process_polylines(para))
            elif draw_type == 'text':
                return HttpResponse(process_text(para))
    return HttpResponse('请使用POST方法')


def process_filter_rgb(para, data):
    limit = para.get('limit', 100)
    c_range = para.get('range', [90, 230])
    if np.max(data) > limit:
        data = data * (255 / np.max(data))
    tmp = np.zeros_like(data)
    tmp[((data > c_range[0]) & (data <= c_range[1]))] = 255
    return tmp


@csrf_exempt
def rgb_color_space(request, color):
    if request.method == 'POST':
        para = json.loads(request.body)
        image_name = para['img']
        img = cv2.imread(PREFIX + image_name)
        data = None
        if color == 'r':
            data = img[:, :, 2]
        elif color == 'g':
            data = img[:, :, 1]
        elif color == 'b':
            data = img[:, :, 0]
        if para.get('filter', False):
            data = process_filter_rgb(para, data)
        filename = color + '-' + image_name
        cv2.imwrite(PREFIX + filename, data)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


@csrf_exempt
def hsv_color_space(request, color):
    if request.method == 'POST':
        para = json.loads(request.body)
        image_name = para['img']
        img = cv2.imread(PREFIX + image_name)
        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
        data = None
        if color == 'h':
            data = hsv[:, :, 0]
        elif color == 's':
            data = hsv[:, :, 1]
        elif color == 'v':
            data = hsv[:, :, 2]
        elif color == 'filter':
            upper_bound = para.get('ub', [0, 0, 0])
            lower_bound = para.get('lb', [200, 40, 100])
            data = cv2.inRange(hsv, np.array(upper_bound), np.array(lower_bound))
        filename = color + '-' + image_name
        cv2.imwrite(PREFIX + filename, data)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


def logic_and(img, has_color, base):
    if base == 2:
        img[0], img[1] = img[1], img[0]
    img1 = cv2.imread(PREFIX + img[0], has_color)
    img2 = cv2.imread(PREFIX + img[1], has_color)
    rows, cols = img1.shape[:2]
    img2 = cv2.resize(img2, (cols, rows), interpolation=cv2.INTER_CUBIC)
    ret = img1 & img2
    filename = getImageName() + DEFAULT_FORMAT
    cv2.imwrite(PREFIX + filename, ret)
    return filename


def logic_or(img, has_color, base):
    if base == 2:
        img[0], img[1] = img[1], img[0]
    img1 = cv2.imread(PREFIX + img[0], has_color)
    img2 = cv2.imread(PREFIX + img[1], has_color)
    rows, cols = img1.shape[:2]
    img2 = cv2.resize(img2, (cols, rows), interpolation=cv2.INTER_CUBIC)
    ret = img1 | img2
    filename = getImageName() + DEFAULT_FORMAT
    cv2.imwrite(PREFIX + filename, ret)
    return filename


def logic_not(img, has_color, base):
    if base == 2:
        img[0], img[1] = img[1], img[0]
    image = cv2.imread(PREFIX + img[0], has_color)
    ret = ~image
    filename = getImageName() + DEFAULT_FORMAT
    cv2.imwrite(PREFIX + filename, ret)
    return filename


def arithmetic_add(img, has_color, base):
    if base == 2:
        img[0], img[1] = img[1], img[0]
    img1 = cv2.imread(PREFIX + img[0], has_color)
    img2 = cv2.imread(PREFIX + img[1], has_color)
    rows, cols = img1.shape[:2]
    img2 = cv2.resize(img2, (cols, rows), interpolation=cv2.INTER_CUBIC)
    ret = cv2.add(img1, img2)
    filename = getImageName() + DEFAULT_FORMAT
    cv2.imwrite(PREFIX + filename, ret)
    return filename


def arithmetic_sub(img, has_color, base):
    img1 = cv2.imread(PREFIX + img[0], has_color)
    img2 = cv2.imread(PREFIX + img[1], has_color)
    if base == 1:
        rows, cols = img1.shape[:2]
        img2 = cv2.resize(img2, (cols, rows), interpolation=cv2.INTER_CUBIC)
    elif base == 2:
        rows, cols = img2.shape[:2]
        img1 = cv2.resize(img1, (cols, rows), interpolation=cv2.INTER_CUBIC)
    ret = cv2.subtract(img1, img2)
    filename = getImageName() + DEFAULT_FORMAT
    cv2.imwrite(PREFIX + filename, ret)
    return filename


def arithmetic_multi(img, has_color, base):
    if base == 2:
        img[0], img[1] = img[1], img[0]
    img1 = cv2.imread(PREFIX + img[0], has_color)
    img2 = cv2.imread(PREFIX + img[1], has_color)
    rows, cols = img1.shape[:2]
    img2 = cv2.resize(img2, (cols, rows), interpolation=cv2.INTER_CUBIC)
    ret = cv2.multiply(img1, img2)
    filename = getImageName() + DEFAULT_FORMAT
    cv2.imwrite(PREFIX + filename, ret)
    return filename


def arithmetic_div(img, has_color, base):
    img1 = cv2.imread(PREFIX + img[0], has_color)
    img2 = cv2.imread(PREFIX + img[1], has_color)
    if base == 1:
        rows, cols = img1.shape[:2]
        img2 = cv2.resize(img2, (cols, rows), interpolation=cv2.INTER_CUBIC)
    elif base == 2:
        rows, cols = img2.shape[:2]
        img1 = cv2.resize(img1, (cols, rows), interpolation=cv2.INTER_CUBIC)
    ret = cv2.divide(img1, img2)
    filename = getImageName() + DEFAULT_FORMAT
    cv2.imwrite(PREFIX + filename, ret)
    return filename


@csrf_exempt
def basic_operation(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        operator = para['operator']
        img = para['img']
        has_color = para['color']
        base = para['base']
        if operator == 'and':
            return HttpResponse(logic_and(img, has_color, base))
        elif operator == 'or':
            return HttpResponse(logic_or(img, has_color, base))
        elif operator == 'not':
            return HttpResponse(logic_not(img, has_color, base))
        elif operator == 'add':
            return HttpResponse(arithmetic_add(img, has_color, base))
        elif operator == 'sub':
            return HttpResponse(arithmetic_sub(img, has_color, base))
        elif operator == 'multi':
            return HttpResponse(arithmetic_multi(img, has_color, base))
        elif operator == 'div':
            return HttpResponse(arithmetic_div(img, has_color, base))
    return HttpResponse('请使用POST方法')


@csrf_exempt
def resize(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image)
        size = para.get('size', None)
        if size is not None:
            width = img.shape[0]
            height = img.shape[1]
            interpolation = None
            if width * height >= size[0] * size[1]:
                interpolation = cv2.INTER_AREA
            else:
                interpolation = cv2.INTER_CUBIC
            img = cv2.resize(img, tuple(size), interpolation)
        else:
            multiple = para['multiple']
            if multiple < 1:
                interpolation = cv2.INTER_AREA
            else:
                interpolation = cv2.INTER_CUBIC
            img = cv2.resize(img, (0, 0), fx=multiple, fy=multiple, interpolation=interpolation)
        new_filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + new_filename, img)
        return HttpResponse(new_filename)
    return HttpResponse('请使用POST方法')


def rotate_bound(image, angle):
    (h, w) = image.shape[:2]
    (cX, cY) = (w // 2, h // 2)
    M = cv2.getRotationMatrix2D((cX, cY), -angle, 1.0)
    cos = np.abs(M[0, 0])
    sin = np.abs(M[0, 1])
    nW = int((h * sin) + (w * cos))
    nH = int((h * cos) + (w * sin))
    M[0, 2] += (nW / 2) - cX
    M[1, 2] += (nH / 2) - cY
    return cv2.warpAffine(image, M, (nW, nH))


@csrf_exempt
def rotate(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image)
        angle = para['angle']
        img = rotate_bound(img, angle)
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


@csrf_exempt
def translation(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image)
        offset = para['offset']
        height, width = img.shape[:2]
        M = np.float32([[1, 0, offset[0]], [0, 1, offset[1]]])
        img = cv2.warpAffine(img, M, (width, height))
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


@csrf_exempt
def flip(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image)
        flip_code = para.get('flip', 1)
        img = cv2.flip(img, flip_code)
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


@csrf_exempt
def affine(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image)
        before = np.float32(para['before'])
        after = np.float32(para['after'])
        rows, cols = img.shape[:2]
        M = cv2.getAffineTransform(before, after)
        img = cv2.warpAffine(img, M, (rows, cols))
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


def hist_cover_gray(img, fileName):
    plt.figure(fileName, figsize=(16, 8))
    hist = cv2.calcHist([img], [0], None, [256], [0, 255])
    plt.plot(hist)
    plt.xlim([0, 255])
    plt.savefig(fileName)


def hist_cover_rgb(img, fileName):
    color = ["r", "g", "b"]
    b, g, r = cv2.split(img)
    img = cv2.merge([r, g, b])
    for index, c in enumerate(color):
        hist = cv2.calcHist([img], [index], None, [256], [0, 255])
        plt.plot(hist, color=c)
        plt.xlim([0, 255])
    plt.savefig(fileName)


@csrf_exempt
def histogram(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']

        color = para['color']
        histogram_name = getImageName() + DEFAULT_FORMAT
        if color:
            img = cv2.imread(PREFIX + image)
            hist_cover_rgb(img, PREFIX + histogram_name)
        else:
            img = cv2.imread(PREFIX + image, 0)
            hist_cover_gray(img, PREFIX + histogram_name)
        return HttpResponse(histogram_name)
    return HttpResponse('请使用POST方法')


def grayHist(img, filename):
    plt.figure(filename, figsize=(16, 8))
    h, w = img.shape[:2]
    pixelSequence = img.reshape(h * w, 1)
    numberBins = 256
    histogram, bins, patch = plt.hist(pixelSequence, numberBins)
    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)


@csrf_exempt
def piecewise_linear_transform(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image, 0)
        funcs = para['func']
        ret_name1 = getImageName() + DEFAULT_FORMAT
        ret_name2 = getImageName() + DEFAULT_FORMAT
        out_name = getImageName() + DEFAULT_FORMAT
        h, w = img.shape[:2]
        out = np.zeros(img.shape, np.uint8)
        for i in range(h):
            for j in range(w):
                pix = img[i][j]
                last_limit = float("-inf")
                for func in funcs:
                    limit = func['limit']
                    k = func['k']
                    b = func['b']
                    if limit == -1:
                        if pix > last_limit:
                            out[i][j] = k * pix + b
                    else:
                        if last_limit <= pix < limit:
                            out[i][j] = k * pix + b
                    last_limit = limit
        out = np.around(out).astype(np.uint8)
        cv2.imwrite(PREFIX + out_name, out)
        grayHist(img, PREFIX + ret_name1)
        grayHist(out, PREFIX + ret_name2)
        return HttpResponse([{"orig": image, "hist": ret_name1}, {"orig": out_name, "hist": ret_name2}])
    return HttpResponse('请使用POST方法')


@csrf_exempt
def edge_detection(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image, 0)
        operator = para['operator']
        if operator == 'Roberts':
            kernelx = np.array([[-1, 0], [0, 1]], dtype=int)
            kernely = np.array([[0, -1], [1, 0]], dtype=int)
            x = cv2.filter2D(img, cv2.CV_16S, kernelx)
            y = cv2.filter2D(img, cv2.CV_16S, kernely)
            absX = cv2.convertScaleAbs(x)
            absY = cv2.convertScaleAbs(y)
            img = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
        elif operator == 'Sobel':
            x = cv2.Sobel(img, cv2.CV_16S, 1, 0)
            y = cv2.Sobel(img, cv2.CV_16S, 0, 1)
            absX = cv2.convertScaleAbs(x)
            absY = cv2.convertScaleAbs(y)
            img = cv2.addWeighted(absX, 0.5, absY, 0.5, 0)
        elif operator == 'Laplacian':
            img_gaussianBlur = cv2.GaussianBlur(img, (5, 5), 0)
            dst = cv2.Laplacian(img_gaussianBlur, cv2.CV_16S, ksize=3)
            img = cv2.convertScaleAbs(dst)
        elif operator == 'LoG':
            grayImage = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            img = cv2.copyMakeBorder(grayImage, 2, 2, 2, 2, borderType=cv2.BORDER_REPLICATE)
            img = cv2.GaussianBlur(img, (3, 3), 0, 0)
            m1 = np.array(
                [[0, 0, -1, 0, 0], [0, -1, -2, -1, 0], [-1, -2, 16, -2, -1], [0, -1, -2, -1, 0], [0, 0, -1, 0, 0]],
                dtype=np.int32)
            image1 = np.zeros(img.shape).astype(np.int32)
            h, w, _ = img.shape
            for i in range(2, h - 2):
                for j in range(2, w - 2):
                    image1[i, j] = np.sum(m1 * img[i - 2:i + 3, j - 2:j + 3, 1])
            img = cv2.convertScaleAbs(image1)
        elif operator == 'Canny':
            tmp = cv2.GaussianBlur(img, (3, 3), 0)
            # 3. 求x，y方向的Sobel算子
            gradx = cv2.Sobel(tmp, cv2.CV_16SC1, 1, 0)
            grady = cv2.Sobel(tmp, cv2.CV_16SC1, 0, 1)
            # 4. 使用Canny函数处理图像，x,y分别是3求出来的梯度，低阈值50，高阈值150
            img = cv2.Canny(gradx, grady, 50, 150)
        elif operator == 'Enhance':
            h, w = img.shape
            gradient = np.zeros((h, w))
            img = img.astype('float')
            for i in range(h - 1):
                for j in range(w - 1):
                    gx = abs(img[i + 1, j] - img[i, j])
                    gy = abs(img[i, j + 1] - img[i, j])
                    gradient[i, j] = gx + gy
            sharp = img + gradient
            sharp = np.where(sharp > 255, 255, sharp)
            sharp = np.where(sharp < 0, 0, sharp)
            gradient = gradient.astype('uint8')
            sharp = sharp.astype('uint8')
            sharp_name = getImageName() + DEFAULT_FORMAT
            gradient_name = getImageName() + DEFAULT_FORMAT
            cv2.imwrite(PREFIX + sharp_name, sharp)
            cv2.imwrite(PREFIX + gradient_name, gradient)
            ret = [{"sharp": sharp_name, "gradient": gradient_name}]
            return HttpResponse(ret)
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


@csrf_exempt
def line_change_detection(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image)
        use_p = para['use_p']

        img = cv2.GaussianBlur(img, (3, 3), 0)
        edges = cv2.Canny(img, 50, 150, apertureSize=3)
        if use_p:
            linesP = cv2.HoughLinesP(edges, 1, np.pi / 180, 80, 200, 15)
            for i_P in linesP:
                for x1, y1, x2, y2 in i_P:
                    cv2.line(img, (x1, y1), (x2, y2), (0, 255, 0), 3)
        else:
            lines = cv2.HoughLines(edges, 1, np.pi / 2, 118)
            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 - img.shape[0] * np.sin(theta)) / np.cos(theta)), img.shape[0])
                        cv2.line(img, pt1, pt2, (0, 0, 255))
                    else:
                        pt1 = (0, int(rho / np.sin(theta)))
                        pt2 = (img.shape[1], int((rho - img.shape[1] * np.cos(theta)) / np.sin(theta)))
                        cv2.line(img, pt1, pt2, (0, 0, 255), 1)
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


def ideal_low_filter(img, D0):
    """
    生成一个理想低通滤波器（并返回）
    """
    h, w = img.shape[:2]
    filter_img = np.ones((h, w))
    u = np.fix(h / 2)
    v = np.fix(w / 2)
    for i in range(h):
        for j in range(w):
            d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
            filter_img[i, j] = 0 if d > D0 else 1
    return filter_img


def ideal_high_pass_filter(img, D0):
    h, w = img.shape[:2]
    filter_img = np.zeros((h, w))
    u = np.fix(h / 2)
    v = np.fix(w / 2)
    for i in range(h):
        for j in range(w):
            d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
            filter_img[i, j] = 0 if d < D0 else 1
    return filter_img


def butterworth_low_filter(img, D0, rank):
    """
        生成一个Butterworth低通滤波器（并返回）
    """
    h, w = img.shape[:2]
    filter_img = np.zeros((h, w))
    u = np.fix(h / 2)
    v = np.fix(w / 2)
    for i in range(h):
        for j in range(w):
            d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
            filter_img[i, j] = 1 / (1 + ((d / D0) ** (2 * rank)))
    return filter_img


def butterworth_high_pass_filter(img, D0, rank):
    """
        生成一个Butterworth低通滤波器（并返回）
    """
    h, w = img.shape[:2]
    filter_img = np.zeros((h, w))
    u = np.fix(h / 2)
    v = np.fix(w / 2)
    for i in range(h):
        for j in range(w):
            d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
            filter_img[i, j] = 1 / (1 + ((D0 / d) ** (2 * rank)))
    return filter_img


def gauss_low_pass_filter(img, d0):
    """
        生成一个指数低通滤波器（并返回）
    """
    h, w = img.shape[:2]
    filter_img = np.zeros((h, w))
    u = np.fix(h / 2)
    v = np.fix(w / 2)
    for i in range(h):
        for j in range(w):
            d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
            filter_img[i, j] = np.exp(-0.5 * (d ** 2) / (d0 ** 2))
    return filter_img


def gauss_high_pass_filter(img, d0):
    """
        生成一个指数低通滤波器（并返回）
    """
    h, w = img.shape[:2]
    filter_img = np.zeros((h, w))
    u = np.fix(h / 2)
    v = np.fix(w / 2)
    for i in range(h):
        for j in range(w):
            d = np.sqrt((i - u) ** 2 + (j - v) ** 2)
            filter_img[i, j] = 1 - np.exp(-0.5 * (d ** 2) / (d0 ** 2))
    return filter_img


def filter_use_smooth(img, filter):
    """
    将图像img与滤波器filter结合，生成对应的滤波图像
    """
    # 首先进行傅里叶变换
    f = np.fft.fft2(img)
    f_center = np.fft.fftshift(f)
    # 应用滤波器进行反变换
    S = np.multiply(f_center, filter)  # 频率相乘——l(u,v)*H(u,v)
    f_origin = np.fft.ifftshift(S)  # 将低频移动到原来的位置
    f_origin = np.fft.ifft2(f_origin)  # 使用ifft2进行傅里叶的逆变换
    f_origin = np.abs(f_origin)  # 设置区间
    return f_origin


def filter_use_sharpen(img, filter):
    f = np.fft.fft2(img)
    f_center = np.fft.fftshift(f)
    # 应用滤波器进行反变换
    S = np.multiply(f_center, filter)  # 频率相乘——l(u,v)*H(u,v)
    f_origin = np.fft.ifftshift(S)  # 将低频移动到原来的位置
    f_origin = np.fft.ifft2(f_origin)  # 使用ifft2进行傅里叶的逆变换
    f_origin = np.abs(f_origin)  # 设置区间
    f_origin = f_origin / np.max(f_origin.all())
    return f_origin


@csrf_exempt
def smooth(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image, 0)
        # frequency domain
        fd = para['fd']
        filter = para['filter']
        if fd:
            if filter == 'ideal_low_pass_filter':
                d0 = para.get('d0', 20)
                img = filter_use_smooth(img, ideal_low_filter(img, d0))
            elif filter == 'butterworth_low_pass_filter':
                d0 = para.get('d0', 20)
                rank = para.get('rank', 2)
                img = filter_use_smooth(img, butterworth_low_filter(img, d0, rank))
            elif filter == 'gauss_low_pass_filter':
                d0 = para.get('d0', 20)
                img = filter_use_smooth(img, gauss_low_pass_filter(img, d0))
        else:
            size = para['size']
            if filter == 'average':
                img = cv2.blur(img, (size, size))
            elif filter == 'median':
                img = cv2.medianBlur(img, size)
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


def process_roberts(img):
    kernelx = np.array([[-1, 0], [0, 1]], dtype=int)
    kernely = np.array([[0, -1], [1, 0]], dtype=int)
    x = cv2.filter2D(img, cv2.CV_16S, kernelx)
    y = cv2.filter2D(img, cv2.CV_16S, kernely)
    # 转uint8
    absX = cv2.convertScaleAbs(x)
    absY = cv2.convertScaleAbs(y)
    return cv2.addWeighted(absX, 0.5, absY, 0.5, 0)


def process_sobel(img):
    x = cv2.Sobel(img, cv2.CV_16S, 1, 0)  # 对x求一阶导
    y = cv2.Sobel(img, cv2.CV_16S, 0, 1)  # 对y求一阶导
    absX = cv2.convertScaleAbs(x)
    absY = cv2.convertScaleAbs(y)
    return cv2.addWeighted(absX, 0.5, absY, 0.5, 0)


def process_prewitt(img):
    kernelx = np.array([[1, 1, 1], [0, 0, 0], [-1, -1, -1]], dtype=int)
    kernely = np.array([[-1, 0, 1], [-1, 0, 1], [-1, 0, 1]], dtype=int)
    x = cv2.filter2D(img, cv2.CV_16S, kernelx)
    y = cv2.filter2D(img, cv2.CV_16S, kernely)
    # 转uint8
    absX = cv2.convertScaleAbs(x)
    absY = cv2.convertScaleAbs(y)
    return cv2.addWeighted(absX, 0.5, absY, 0.5, 0)


def process_laplacian(img):
    dst = cv2.Laplacian(img, cv2.CV_16S, ksize=3)
    return cv2.convertScaleAbs(dst)


@csrf_exempt
def sharpen(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image, 0)
        # frequency domain
        fd = para['fd']
        filter = para['filter']
        if fd:
            if filter == 'ideal_high_pass_filter':
                d0 = para.get('d0', 40)
                img = filter_use_sharpen(img, ideal_high_pass_filter(img, d0))
            elif filter == 'butterworth_high_pass_filter':
                d0 = para.get('d0', 20)
                rank = para.get('rank', 2)
                img = filter_use_sharpen(img, butterworth_high_pass_filter(img, d0, rank))
            elif filter == 'gauss_high_pass_filter':
                d0 = para.get('d0', 20)
                img = filter_use_sharpen(img, gauss_high_pass_filter(img, d0))
        else:
            if filter == 'Roberts':
                img = process_roberts(img)
            elif filter == 'Sobel':
                img = process_sobel(img)
            elif filter == 'Prewitt':
                img = process_prewitt(img)
            elif filter == 'Laplacian':
                img = process_laplacian(img)
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


@csrf_exempt
def morphology(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        size = para.get('size', 5)
        img = cv2.imread(PREFIX + image, cv2.IMREAD_UNCHANGED)
        kernel = cv2.getStructuringElement(cv2.MORPH_CROSS, (size, size))
        op = para['type']
        if op == 'open':
            img = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
        elif op == 'close':
            img = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
        elif op == 'erode':
            img = cv2.erode(img, kernel)
        elif op == 'dilate':
            img = cv2.dilate(img, kernel)
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


@csrf_exempt
def noise(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image)
        op = para['type']
        if op == 'gauss':
            img = np.array(img / 255, dtype=float)
            noise = np.random.normal(0, 0.1, img.shape)
            img = img + noise
            img = np.clip(img, 0.0, 1.0)
            img = np.uint8(img * 255)
        elif op == 'salt_and_pepper':
            range_salt = para.get('salt', 0.2)
            range_peper = para.get('peper', 0.8)
            output = np.zeros(img.shape, np.uint8)
            for i in range(img.shape[0]):
                for j in range(img.shape[1]):
                    rdn = random.random()
                    if rdn < range_salt:
                        output[i][j] = 0
                    elif rdn > range_peper:
                        output[i][j] = 255
                    else:
                        output[i][j] = img[i][j]
            img = output
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


def process_geometric_mean(img):
    output = np.zeros(img.shape, np.uint8)
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            ji = 1.0
            for m in range(-1, 2):
                if 0 <= j + m < img.shape[1]:
                    ji *= img[i][j + m]
            output[i][j] = math.pow(ji, 1 / 3)
    return output


def process_arithmetic_mean(img):
    output = np.zeros(img.shape, np.uint8)
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            sum = 0
            for m in range(-1, 2):
                for n in range(-1, 2):
                    if 0 <= i + m < img.shape[0] and 0 <= j + n < img.shape[1]:
                        sum += img[i + m][j + n]
            output[i][j] = int(sum / 9)
    return output


def process_harmonic_filter(img):
    output = np.zeros(img.shape, np.uint8)
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            sum = 0
            for m in range(-1, 2):
                for n in range(-1, 2):
                    if 0 <= i + m < img.shape[0] and 0 <= j + n < img.shape[1]:
                        sum += 1 / img[i + m][j + n]
            output[i][j] = int(9 / sum)
    return output


def get_middle(array):
    # 列表的长度
    length = len(array)
    # 对列表进行选择排序，获得有序的列表
    for i in range(length):
        for j in range(i + 1, length):
            # 选择最大的值
            if array[j] > array[i]:
                # 交换位置
                temp = array[j]
                array[j] = array[i]
                array[i] = temp
    return array[int(length / 2)]


def process_max_sort(img):
    output = np.zeros(img.shape, np.uint8)
    array = []
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            array.clear()
            for m in range(-1, 2):
                for n in range(-1, 2):
                    if 0 <= i + m < img.shape[0] and 0 <= j + n < img.shape[1]:
                        array.append(img[i + m][j + n])
            array.sort(reverse=True)
            output[i][j] = max(array[0], img[i][j])
    return output


def process_min_sort(img):
    output = np.zeros(img.shape, np.uint8)
    array = []
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            array.clear()
            for m in range(-1, 2):
                for n in range(-1, 2):
                    if 0 <= i + m < img.shape[0] and 0 <= j + n < img.shape[1]:
                        array.append(img[i + m][j + n])
            array.sort()
            output[i][j] = min(array[0], img[i][j])
    return output


def process_median_sort(img):
    output = np.zeros(img.shape, np.uint8)
    array = []
    for i in range(img.shape[0]):
        for j in range(img.shape[1]):
            array.clear()
            for m in range(-1, 2):
                for n in range(-1, 2):
                    if 0 <= i + m < img.shape[0] and 0 <= j + n < img.shape[1]:
                        array.append(img[i + m][j + n])
            output[i][j] = get_middle(array)
    return output


@csrf_exempt
def filtration(request):
    if request.method == 'POST':
        para = json.loads(request.body)
        image = para['img']
        img = cv2.imread(PREFIX + image, 0)
        op = para['type']
        detail_type = para['detail_type']
        if op == 'mean':
            if detail_type == 'arithmetic':
                img = process_arithmetic_mean(img)
            elif detail_type == 'geometric':
                img = process_geometric_mean(img)
            elif detail_type == 'harmonic':
                img = process_harmonic_filter(img)
        elif op == 'sort':
            if detail_type == 'max':
                img = process_max_sort(img)
            elif detail_type == 'median':
                img = process_median_sort(img)
            elif detail_type == 'min':
                img = process_min_sort(img)
        filename = getImageName() + DEFAULT_FORMAT
        cv2.imwrite(PREFIX + filename, img)
        return HttpResponse(filename)
    return HttpResponse('请使用POST方法')


def r(request):
    return render(request, 'upload.html')