import cv2
import numpy as np
from django.http import JsonResponse
import base64
import tensorflow
from django.views.decorators.http import require_POST

def base64tocv(imagecode):
    strList = str(imagecode).split(',')
    b64_img = ''
    for i in strList[1:]:
        b64_img += i
    imgString = base64.b64decode(b64_img)
    nparr = np.fromstring(imgString, np.uint8)
    image = cv2.imdecode(nparr, cv2.COLOR_RGB2BGR)
    # image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
    return image

#ndarray 转 base64
def getbase64byndarray(pic_img):
    retval, buffer = cv2.imencode('.jpg', pic_img)
    pic_str = base64.b64encode(buffer)
    return pic_str.decode()


@require_POST
def style_transfer(request):
    '''
    pathIn: 原始图片的路径
    pathOut: 风格化图片的保存路径
    model: 预训练模型的路径
    width: 设置风格化图片的宽度，默认为None, 即原始图片尺寸
    jpg_quality: 0-100，设置输出图片的质量，默认80，越大图片质量越好
    '''

    ## 读入原始图片，调整图片至所需尺寸，然后获取图片的宽度和高度
    image = request.POST.get('files')
    img = base64tocv(image)
    (h, w) = img.shape[:2]

    ## 从本地加载预训练模型
    net = get_model_from_style(request.POST.get('style'))

    ## 将图片构建成一个blob：设置图片尺寸，将各通道像素值减去平均值（比如ImageNet所有训练样本各通道统计平均值）
    ## 然后执行一次前馈网络计算，并输出计算所需的时间
    blob = cv2.dnn.blobFromImage(img, 1.0, (w, h), (103.939, 116.779, 123.680), swapRB=False, crop=False)
    net.setInput(blob)
    output = net.forward()

    ## reshape输出结果, 将减去的平均值加回来，并交换各颜色通道
    output = output.reshape((3, output.shape[2], output.shape[3]))
    output[0] += 103.939
    output[1] += 116.779
    output[2] += 123.680
    output = output.transpose(1, 2, 0)
    output=getbase64byndarray(output)

    return JsonResponse(data={"image": output, "height": h, "width": w},
                        json_dumps_params={'ensure_ascii': False})

# model_base_dir = "/root/imageProcess/styleTransfer/models/"
model_base_dir = "F:\imageprocess\imageProcess\styleTransfer\models\\"
d_model_map = {
    0: "candy",
    1: "udnie",
    2: "la_muse",
    3: "the_scream",
    4: "mosaic",
    5: "feathers",
    6: "starry_night"
}

def get_model_from_style(style):
    """
    加载指定风格的模型
    :param style: 模型编码
    :return: model
    """
    style=int(style)
    model_name = d_model_map[style]
    model_path = model_base_dir + model_name + ".t7"
    model = cv2.dnn.readNetFromTorch(model_path)
    return model