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import cv2
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import numpy as np
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from django.http import JsonResponse
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import base64
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import tensorflow
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from django.views.decorators.http import require_POST
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def base64tocv(imagecode):
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strList = str(imagecode).split(',')
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b64_img = ''
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for i in strList[1:]:
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b64_img += i
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imgString = base64.b64decode(b64_img)
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nparr = np.fromstring(imgString, np.uint8)
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image = cv2.imdecode(nparr, cv2.COLOR_RGB2BGR)
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# image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
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return image
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#ndarray 转 base64
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def getbase64byndarray(pic_img):
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retval, buffer = cv2.imencode('.jpg', pic_img)
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pic_str = base64.b64encode(buffer)
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return pic_str.decode()
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@require_POST
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def style_transfer(request):
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'''
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pathIn: 原始图片的路径
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pathOut: 风格化图片的保存路径
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model: 预训练模型的路径
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width: 设置风格化图片的宽度，默认为None, 即原始图片尺寸
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jpg_quality: 0-100，设置输出图片的质量，默认80，越大图片质量越好
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'''
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## 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
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image = request.POST.get('files')
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img = base64tocv(image)
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(h, w) = img.shape[:2]
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## 从本地加载预训练模型
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net = get_model_from_style(request.POST.get('style'))
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## 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）
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## 然后执行一次前馈网络计算，并输出计算所需的时间
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blob = cv2.dnn.blobFromImage(img, 1.0, (w, h), (103.939, 116.779, 123.680), swapRB=False, crop=False)
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net.setInput(blob)
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output = net.forward()
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## reshape输出结果, 将减去的平均值加回来，并交换各颜色通道
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output = output.reshape((3, output.shape[2], output.shape[3]))
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output[0] += 103.939
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output[1] += 116.779
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output[2] += 123.680
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output = output.transpose(1, 2, 0)
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output=getbase64byndarray(output)
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return JsonResponse(data={"image": output, "height": h, "width": w},
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json_dumps_params={'ensure_ascii': False})
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# model_base_dir = "/root/imageProcess/styleTransfer/models/"
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model_base_dir = "F:\imageprocess\imageProcess\styleTransfer\models\\"
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d_model_map = {
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0: "candy",
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1: "udnie",
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2: "la_muse",
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3: "the_scream",
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4: "mosaic",
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5: "feathers",
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6: "starry_night"
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}
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def get_model_from_style(style):
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"""
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加载指定风格的模型
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:param style: 模型编码
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:return: model
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"""
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style=int(style)
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model_name = d_model_map[style]
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model_path = model_base_dir + model_name + ".t7"
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model = cv2.dnn.readNetFromTorch(model_path)
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return model
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