2022/7/28

imageProcess/actionDrive/admin.py

@@ -1,3 +0,0 @@
-from django.contrib import admin
-
-# Register your models here.

imageProcess/actionDrive/apps.py

@@ -1,6 +0,0 @@
-from django.apps import AppConfig
-
-
-class ActiondriveConfig(AppConfig):
-    default_auto_field = 'django.db.models.BigAutoField'
-    name = 'actionDrive'

imageProcess/actionDrive/models.py

@@ -1,3 +0,0 @@
-from django.db import models
-
-# Create your models here.

imageProcess/actionDrive/tests.py

@@ -1,3 +0,0 @@
-from django.test import TestCase
-
-# Create your tests here.

imageProcess/actionDrive/views.py

@@ -1,3 +0,0 @@
-from django.shortcuts import render
-
-# Create your views here.

imageProcess/ageTransfer/admin.py

@@ -1,3 +0,0 @@
-from django.contrib import admin
-
-# Register your models here.

imageProcess/ageTransfer/apps.py

@@ -1,6 +0,0 @@
-from django.apps import AppConfig
-
-
-class AgetransferConfig(AppConfig):
-    default_auto_field = 'django.db.models.BigAutoField'
-    name = 'ageTransfer'

imageProcess/ageTransfer/models.py

@@ -1,3 +0,0 @@
-from django.db import models
-
-# Create your models here.

imageProcess/ageTransfer/tests.py

@@ -1,3 +0,0 @@
-from django.test import TestCase
-
-# Create your tests here.

imageProcess/ageTransfer/views.py

@@ -1,3 +0,0 @@
-from django.shortcuts import render
-
-# Create your views here.

imageProcess/basicFunction/views.py

@@ -1,16 +1,18 @@
-import cv2
-import numpy as np
-from django.core.files.uploadedfile import InMemoryUploadedFile
-from django.http import HttpResponse
+
+from removebg import RemoveBg
 from django.http import JsonResponse
 from django.views.decorators.http import require_POST,require_GET
+from matplotlib import pyplot as plt
 import sys
-sys.path.append("..")
+import imutils
+from PIL import Image
+sys.path.append("../")
 import urllib.request
 import base64
 import cv2
 import numpy as np
-
+import numba as nb
+from pyzbar.pyzbar import decode
 # base64转cv
 def base64tocv(imagecode):
     strList = str(imagecode).split(',')
@@ -23,14 +25,14 @@ def base64tocv(imagecode):
     # image = cv2.imdecode(nparr, cv2.IMREAD_COLOR)
     return image
 
-#cv 转 base64
-def cvtobase64(img):
-    #
-    # image = cv2.imencode('.jpg', img)[1]
-    # res_bs64 = str(base64.b64encode(image))[2:-1]
-    res_b = cv2.imencode('.jpg', img)[1].tostring()
-    res_bs64 = base64.b64encode(res_b)
-    return res_bs64
+# #cv 转 base64
+# def cvtobase64(img):
+#     #
+#     # image = cv2.imencode('.jpg', img)[1]
+#     # res_bs64 = str(base64.b64encode(image))[2:-1]
+#     res_b = cv2.imencode('.jpg', img)[1].tostring()
+#     res_bs64 = base64.b64encode(res_b)
+#     return res_bs64
 
 #InMemoryUploadedFile 转 ndarray
 def getNdarraybyInMemoryUploadedFile(uploaded_file):
@@ -97,12 +99,171 @@ def rotatePicture(request):
     image = request.POST.get('files')
     cv_image = base64tocv(image)
     rows, cols, depth = cv_image.shape
+    (cX, cY) = (cols // 2, rows // 2)
     direction=request.POST.get('direction')
     if(direction=='left'):
         M = cv2.getRotationMatrix2D((cols / 2, rows / 2), 90, 1)
-        cv_image = cv2.warpAffine(cv_image, M, (cols, rows))
+        cos = np.abs(M[0, 0])
+        sin = np.abs(M[0, 1])
+        nW = int((rows * sin) + (cols * cos))
+        nH = int((rows * cos) + (cols * sin))
+        M[0, 2] += (nW / 2) - cX
+        M[1, 2] += (nH / 2) - cY
+        cv_image = cv2.warpAffine(cv_image, M, (nW, nH),borderValue=(255,255,255))
     elif(direction=='right'):
         M = cv2.getRotationMatrix2D((cols / 2, rows / 2), -90, 1)
-        cv_image = cv2.warpAffine(cv_image, M, (cols, rows))
+        cos = np.abs(M[0, 0])
+        sin = np.abs(M[0, 1])
+        nW = int((rows * sin) + (cols * cos))
+        nH = int((rows * cos) + (cols * sin))
+        M[0, 2] += (nW / 2) - cX
+        M[1, 2] += (nH / 2) - cY
+        cv_image = cv2.warpAffine(cv_image, M, (nW, nH),borderValue=(255,255,255))
     rotate_image=getbase64byndarray(cv_image)
-    return JsonResponse(data={"image": rotate_image}, json_dumps_params={'ensure_ascii': False})
+    return JsonResponse(data={"image": rotate_image}, json_dumps_params={'ensure_ascii': False})
+
+#抠图
+@require_POST
+def keying(request):
+    rmbg = RemoveBg("XHfvSY3phCVna4NZj8Tr8Wnz", "error.log")
+    image = request.POST.get('files')
+    rmbg.remove_background_from_base64_img(image,new_file_name="output.jpg",bg_color=(0,0,0))
+    result=cv2.imread("output.jpg")
+    result=getbase64byndarray(result)
+    return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
+
+#图像平滑
+@require_POST
+def smooth(request):
+    base_image = request.POST.get('files')
+    cv_image=base64tocv(base_image)
+    imgarray = np.array(cv_image)
+    height, width = imgarray.shape[0], imgarray.shape[1]
+
+    edge = int((3 - 1) / 2)
+    if height - 1 - edge <= edge or width - 1 - edge <= edge:
+        print("the kenerl is to long")
+        return None
+    for i in range(height):
+        for j in range(width):
+            if i <= edge - 1 or i >= height - 1 - edge or j <= edge - 1 or j >= height - 1 - edge:
+                imgarray[i][j] = imgarray[i][j]
+            else:
+                num = []
+                for m in range(i - edge, i + edge + 1):
+                    for n in range(j - edge, j + edge + 1):
+                        num.append((imgarray[m][n])[0])  # 这里通过彩色图像第一个值计算中值也可以改为第二个或者第三个
+                temp = np.median(num)
+                idex_tem = num.index(temp)  # 获取中值在数组中的坐标
+                l1 = int((idex_tem / 3)) - edge + i  # 根据进制转换反推出中值在图像中的坐标
+                l2 = (idex_tem % 3) - edge + j
+                imgarray[i][j] = imgarray[l1][l2]  # 赋值
+                # num1 = np.sort(num)[int(self.k * self.k / 2)]
+                # print(idex_tem)
+                # temp = np.median(imgarray[i][j], imgarray[i - edge][j], imgarray[i - edge][j])
+                # imgarray[i][j] = np.median(imgarray[i - edge:i + edge + 1, j -edge:j+edge + 1])
+
+
+    new_img = Image.fromarray(imgarray)
+    result=getbase64byndarray(imgarray)
+    return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
+
+
+#曝光调整
+def exposure(request):
+    img = request.POST.get('files')
+    image = base64tocv(img)
+    # chans = cv2.split(image)
+    # colors = ("b", "g", "r")
+    # for (chan, color) in zip(chans, colors):
+    #     hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
+    #     # plt.plot(hist, color=color)
+    #     # plt.xlim([0, 256])
+
+    # plt.subplot(4, 2, 5)
+    equalizeOver = np.zeros(image.shape, image.dtype)
+    equalizeOver[:, :, 0] = cv2.equalizeHist(image[:, :, 0])
+    equalizeOver[:, :, 1] = cv2.equalizeHist(image[:, :, 1])
+    equalizeOver[:, :, 2] = cv2.equalizeHist(image[:, :, 2])
+
+    output = getbase64byndarray(equalizeOver)
+    return JsonResponse(data={"image": output}, json_dumps_params={'ensure_ascii': False})
+
+#锐化
+def sharpen(request):
+    img = request.POST.get('files')
+    image = base64tocv(img)
+
+    sharpen_op = np.array([[0, -1, 0], [-1, 5, -1], [0, -1, 0]], dtype=np.float32)
+    # sharpen_op = np.array([[0, -1, 0], [-1, 4, -1], [0, -1, 0]], dtype=np.float32)
+    sharpen_image = cv2.filter2D(image, cv2.CV_32F, sharpen_op)
+    sharpen_image = cv2.convertScaleAbs(sharpen_image)
+
+    output = getbase64byndarray(sharpen_image)
+    return JsonResponse(data={"image": output}, json_dumps_params={'ensure_ascii': False})
+
+#识别条形码
+def barCode(request):
+    img = request.POST.get('files')
+    image = base64tocv(img)
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    cv2.imshow("Image", gray)
+    cv2.waitKey(0)
+
+    # 计算图片x和y方向的Scharr梯度大小
+    ddepth = cv2.cv.CV_32F if imutils.is_cv2() else cv2.CV_32F
+    gradX = cv2.Sobel(gray, ddepth=ddepth, dx=1, dy=0, ksize=-1)
+    gradY = cv2.Sobel(gray, ddepth=ddepth, dx=0, dy=1, ksize=-1)
+
+    # 用x方向的梯度减去y方向的梯度
+    gradient = cv2.subtract(gradX, gradY)
+    gradient = cv2.convertScaleAbs(gradient)
+    cv2.imshow("gradient", gradient)
+    cv2.waitKey(0)
+
+    blurred = cv2.blur(gradient, (9, 9))
+    (_, thresh) = cv2.threshold(blurred, 180, 255, cv2.THRESH_BINARY)
+    cv2.imshow("blur", blurred)
+    cv2.waitKey(0)
+    cv2.imshow("thresh", thresh)
+    cv2.waitKey(0)
+    # 　构造一个闭合核并应用于阈值图片
+    kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (21, 10))
+    closed = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)
+    cv2.imshow("closed", closed)
+    cv2.waitKey(0)
+
+    # 执行一系列的腐蚀和膨胀操作
+    closed = cv2.dilate(closed, None, iterations=4)
+    closed = cv2.erode(closed, None, iterations=4)
+    cv2.imshow("Image", closed)
+    cv2.waitKey(0)
+
+    # 找到阈值化后图片中的轮廓，然后进行根据区域进行排序，仅保留最大区域
+    cnts = cv2.findContours(closed.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+    cnts = imutils.grab_contours(cnts)
+    c = sorted(cnts, key=cv2.contourArea, reverse=True)[0]
+
+    # 计算最大轮廓的旋转边界框
+    rect = cv2.minAreaRect(c)
+    box = cv2.cv.BoxPoints(rect) if imutils.is_cv2() else cv2.boxPoints(rect)
+    box = np.int0(box)
+
+    # 在检测到的条形码周围绘制边界框并显示图片
+    cv2.drawContours(image, [box], -1, (0, 255, 0), 3)
+    result=getbase64byndarray(image)
+
+    return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
+
+#噪声去除
+def removeNoise(request):
+    img = request.POST.get('files')
+    img = base64tocv(img)
+
+    specular = cv2.fastNlMeansDenoisingColored(img, None, 10, 5, 7, 21)
+
+    result = getbase64byndarray(specular)
+    return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
+
+
+

imageProcess/error.log

@@ -0,0 +1,179 @@
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 131, in keying
+    result=cvtobase64(image)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 32, in cvtobase64
+    res_b = cv2.imencode('.jpg', img)[1].tostring()
+cv2.error: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\imgcodecs\src\loadsave.cpp:976: error: (-215:Assertion failed) !image.empty() in function 'cv::imencode'
+
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 131, in keying
+    result=cvtobase64(image)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 32, in cvtobase64
+    res_b = cv2.imencode('.jpg', img)[1].tostring()
+cv2.error: OpenCV(4.6.0) D:\a\opencv-python\opencv-python\opencv\modules\imgcodecs\src\loadsave.cpp:976: error: (-215:Assertion failed) !image.empty() in function 'cv::imencode'
+
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 132, in keying
+    return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\http\response.py", line 603, in __init__
+    data = json.dumps(data, cls=encoder, **json_dumps_params)
+  File "C:\Users\ASUS\anaconda3\lib\json\__init__.py", line 234, in dumps
+    return cls(
+  File "C:\Users\ASUS\anaconda3\lib\json\encoder.py", line 199, in encode
+    chunks = self.iterencode(o, _one_shot=True)
+  File "C:\Users\ASUS\anaconda3\lib\json\encoder.py", line 257, in iterencode
+    return _iterencode(o, 0)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\serializers\json.py", line 105, in default
+    return super().default(o)
+  File "C:\Users\ASUS\anaconda3\lib\json\encoder.py", line 179, in default
+    raise TypeError(f'Object of type {o.__class__.__name__} '
+TypeError: Object of type bytes is not JSON serializable
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 129, in keying
+    rmbg.remove_background_from_base64_img(image,bg_color=(0,0,0))
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\removebg\removebg.py", line 72, in remove_background_from_base64_img
+    response.raise_for_status()
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\requests\models.py", line 943, in raise_for_status
+    raise HTTPError(http_error_msg, response=self)
+requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://api.remove.bg/v1.0/removebg
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 129, in keying
+    rmbg.remove_background_from_base64_img(image,bg_color=(0,0,0))
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\removebg\removebg.py", line 72, in remove_background_from_base64_img
+    response.raise_for_status()
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\requests\models.py", line 943, in raise_for_status
+    raise HTTPError(http_error_msg, response=self)
+requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://api.remove.bg/v1.0/removebg
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 128, in keying
+    result=getbase64byndarray(image)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 42, in getbase64byndarray
+    retval, buffer = cv2.imencode('.jpg', pic_img)
+cv2.error: OpenCV(4.6.0) :-1: error: (-5:Bad argument) in function 'imencode'
+> Overload resolution failed:
+>  - img is not a numpy array, neither a scalar
+>  - Expected Ptr<cv::UMat> for argument 'img'
+
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 128, in keying
+    result=getbase64byndarray(image)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 42, in getbase64byndarray
+    retval, buffer = cv2.imencode('.jpg', pic_img)
+cv2.error: OpenCV(4.6.0) :-1: error: (-5:Bad argument) in function 'imencode'
+> Overload resolution failed:
+>  - img is not a numpy array, neither a scalar
+>  - Expected Ptr<cv::UMat> for argument 'img'
+
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 129, in keying
+    return JsonResponse(data={"image": result}, json_dumps_params={'ensure_ascii': False})
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\http\response.py", line 603, in __init__
+    data = json.dumps(data, cls=encoder, **json_dumps_params)
+  File "C:\Users\ASUS\anaconda3\lib\json\__init__.py", line 234, in dumps
+    return cls(
+  File "C:\Users\ASUS\anaconda3\lib\json\encoder.py", line 199, in encode
+    chunks = self.iterencode(o, _one_shot=True)
+  File "C:\Users\ASUS\anaconda3\lib\json\encoder.py", line 257, in iterencode
+    return _iterencode(o, 0)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\serializers\json.py", line 105, in default
+    return super().default(o)
+  File "C:\Users\ASUS\anaconda3\lib\json\encoder.py", line 179, in default
+    raise TypeError(f'Object of type {o.__class__.__name__} '
+TypeError: Object of type ndarray is not JSON serializable
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 127, in keying
+    rmbg.remove_background_from_base64_img(image,new_file_name="output.jpg",bg_color=(0,0,0))
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\removebg\removebg.py", line 72, in remove_background_from_base64_img
+    response.raise_for_status()
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\requests\models.py", line 943, in raise_for_status
+    raise HTTPError(http_error_msg, response=self)
+requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://api.remove.bg/v1.0/removebg
+ERROR:django.request:Internal Server Error: /keying/
+Traceback (most recent call last):
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\exception.py", line 47, in inner
+    response = get_response(request)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\core\handlers\base.py", line 181, in _get_response
+    response = wrapped_callback(request, *callback_args, **callback_kwargs)
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\django\views\decorators\http.py", line 40, in inner
+    return func(request, *args, **kwargs)
+  File "F:\imageprocess\imageProcess\basicFunction\views.py", line 127, in keying
+    rmbg.remove_background_from_base64_img(image,new_file_name="output.jpg",bg_color=(0,0,0))
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\removebg\removebg.py", line 72, in remove_background_from_base64_img
+    response.raise_for_status()
+  File "C:\Users\ASUS\anaconda3\lib\site-packages\requests\models.py", line 943, in raise_for_status
+    raise HTTPError(http_error_msg, response=self)
+requests.exceptions.HTTPError: 400 Client Error: Bad Request for url: https://api.remove.bg/v1.0/removebg
+INFO:django.utils.autoreload:F:\imageprocess\imageProcess\basicFunction\views.py changed, reloading.

imageProcess/imageProcess/urls.py

@@ -3,13 +3,21 @@ from django.contrib import admin
 from django.urls import path
 from django.conf.urls import url
 import sys
-sys.path.append("..")
+sys.path.append("../")
 from basicFunction import views as basic_function_view
-
+from styleTransfer import views as style_transfer_view
 urlpatterns = [
     path('admin/', admin.site.urls),
     path('show/', basic_function_view.getPicture),
     path('resize/', basic_function_view.resizePicture),
     path('mirror/', basic_function_view.mirrorPicture),
     path('rotate/', basic_function_view.rotatePicture),
+    path('keying/', basic_function_view.keying),
+    path('styleTransfer/', style_transfer_view.style_transfer),
+    path('smooth/', basic_function_view.smooth),
+    path('exposure/', basic_function_view.exposure),
+    path('sharpen/', basic_function_view.sharpen),
+    path('barCode/', basic_function_view.barCode),
+    path('removeNoise/', basic_function_view.removeNoise),
+
 ]

imageProcess/styleTransfer/constant.py

@@ -0,0 +1,36 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+"""
+Copyright (C) 2021. Huawei Technologies Co., Ltd. All rights reserved.
+
+This program is free software; you can redistribute it and/or modify
+it under the terms of the Apache License Version 2.0.You may not use this file
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
+Apache License for more details at
+http://www.apache.org/licenses/LICENSE-2.0
+"""
+
+
+class Constant:
+    # error code
+    ACL_ERROR_NONE = 0
+
+    # rule for mem
+    ACL_MEM_MALLOC_HUGE_FIRST = 0
+    ACL_MEM_MALLOC_HUGE_ONLY = 1
+    ACL_MEM_MALLOC_NORMAL_ONLY = 2
+
+    # rule for memory copy
+    ACL_MEMCPY_HOST_TO_HOST = 0
+    ACL_MEMCPY_HOST_TO_DEVICE = 1
+    ACL_MEMCPY_DEVICE_TO_HOST = 2
+    ACL_MEMCPY_DEVICE_TO_DEVICE = 3
+
+    # images format
+    IMG_EXT = ['.jpg', '.JPG', '.png', '.PNG', '.bmp', '.BMP', '.jpeg', '.JPEG']
+
+    # numpy data type
+    NPY_FLOAT32 = 11

imageProcess/styleTransfer/method.py

@@ -0,0 +1,135 @@
+
+
+
+import numpy as np
+import mindspore
+# import moxing as mox
+from mindspore import numpy
+from PIL import Image
+from mindspore import ops
+import os
+import cv2 as cv
+import mindspore.dataset as ds
+import mindspore.dataset.vision.c_transforms as c_version
+import mindspore.dataset.vision.py_transforms as py_vision
+from mindspore import Model, context, nn, Tensor, Parameter, load_checkpoint
+
+
+def save_img(i, optimizer, output_path):
+    if not os.path.exists(output_path):
+        os.mkdir(output_path)
+    final_img = optimizer.parameters[0].asnumpy()
+    final_img = final_img.squeeze(axis=0)
+    final_img = np.moveaxis(final_img, 0, 2)
+    dump_img = np.copy(final_img)
+    dump_img += np.array([123.675, 116.28, 103.53]).reshape((1, 1, 3))
+    dump_img = np.clip(dump_img, 0, 255).astype('uint8')
+    # dump_img = cv.resize(dump_img, (224, 224), interpolation=cv.INTER_CUBIC)
+    img_path = output_path+"/"+"iter_"+str(i)+".jpg"    # imgput_path = ./output_path/content2_to_sty3/lr=0.5/iter_1
+    cv.imwrite(img_path, dump_img[:, :, ::-1])
+    # mox.file.copy_parallel(img_path, args.train_url+img_path[12:])
+
+
+def create_dataset(img):
+    """生成数据集"""
+    dataset = ds.NumpySlicesDataset(data=img, column_names=['data'])
+    return dataset
+
+
+def gram_matrix(x, should_normalize=True):
+    """
+    Generate gram matrices of the representations of content and style images.
+    """
+    # 对网络的特征进行矩阵编码
+    b, ch, h, w = x.shape  # x的形状
+    features = x.view(b, ch, w * h)   # 将x降维
+    transpose = ops.Transpose()
+    batmatmul = ops.BatchMatMul(transpose_a=False)
+    features_t = transpose(features, (0, 2, 1))
+    gram = batmatmul(features, features_t)   # gram 为矩阵相乘计算得新图片的像素
+    if should_normalize:  # 标准化
+        gram /= ch * h * w
+    return gram
+
+
+def load_image(img_path, target_shape=None):
+    # 图像预处理 返回 1 * 3 * 400 * x
+    if not os.path.exists(img_path):
+        raise Exception(f'Path not found: {img_path}')
+    img = cv.imread(img_path)[:, :, ::-1]  # convert BGR to RGB when reading
+    if target_shape is not None:
+        if isinstance(target_shape, int) and target_shape != -1:
+            current_height, current_width = img.shape[:2]
+            new_height = target_shape
+            new_width = int(current_width * (new_height / current_height))
+            img = cv.resize(img, (new_width, new_height), interpolation=cv.INTER_CUBIC)
+        else:
+            img = cv.resize(img, (target_shape[1], target_shape[0]), interpolation=cv.INTER_CUBIC)
+    img = img.astype(np.float32)
+
+    to_tensor = py_vision.ToTensor()  # channel conversion and pixel value normalization
+    normalize = c_version.Normalize(mean=[123.675, 116.28, 103.53], std=[1, 1, 1])
+    img = normalize(img)   # <class 'numpy.ndarray'> (400, 533, 3)
+    img = to_tensor(img) * 225     # <class 'numpy.ndarray'> (3, 400, 533)
+    img = np.expand_dims(img, axis=0)
+    # img /= 255
+    # transform = transforms.Compose([
+    #     transforms.ToTensor(),
+    #     transforms.Lambda(lambda x: x.mul(255)),  # 乘255
+    #     transforms.Normalize(mean=[123.675, 116.28, 103.53], std=[1, 1, 1])
+    # ])
+    # img = transform(img).unsqueeze(0)
+    # img = img.numpy()
+    return img
+
+
+class Optim_Loss(nn.Cell):
+    def __init__(self, net, target_maps):
+        super(Optim_Loss, self).__init__()
+        self.net = net
+        self.target_maps = target_maps[:-1]
+        self.weight = [100000.0, 30000.0, 1.0]
+        self.get_style_loss = nn.MSELoss(reduction='sum')
+        self.get_content_loss = nn.MSELoss(reduction='mean')
+        self.cast = ops.Cast()      # 转换为 mindspore.tensor
+        self.ct = target_maps[2]
+
+    def construct(self):
+        optimize_img = self.ct
+        current_maps = self.net(self.cast(optimize_img, mindspore.float32))         # 6个特征图
+        # 当前图片的特征
+        current_content_maps = current_maps[4].squeeze(axis=0)  # 内容特征    # 4_2的内容map
+        for i in range(len(current_maps)):  # 0, 1, 2, 3, 4, 5    1, 2, 3, 4, 4_2, 5
+            if i != 4:
+                current_maps[i] = gram_matrix(current_maps[i])
+        target_content_maps = self.target_maps[0]  # 任务的内容特征
+        target_content_gram = self.target_maps[1]  # 任务的风格特征
+        content_loss = self.get_content_loss(current_content_maps, target_content_maps)
+        style_loss = 0
+        for j in range(6):
+            if j == 5:
+                style_loss += self.get_style_loss(current_maps[j], target_content_gram[j-1])
+            if j < 4:
+                style_loss += self.get_style_loss(current_maps[j], target_content_gram[j])
+
+        style_loss /= 5
+        tv_loss = numpy.sum(numpy.abs(optimize_img[:, :, :, :-1] - optimize_img[:, :, :, 1:])) \
+                  + numpy.sum(numpy.abs(optimize_img[:, :, :-1, :] - optimize_img[:, :, 1:, :]))
+
+        total_loss = content_loss * self.weight[0] + style_loss * self.weight[1] + tv_loss * self.weight[2]
+        return total_loss/130001
+
+
+def load_parameters(file_name):
+    param_dict = load_checkpoint(file_name)
+    param_dict_new = {}
+    # print(param_dict)
+    for key, values in param_dict.items():
+        if key.startswith('moments.'):
+            continue
+        elif key.startswith("layers."):
+            param_dict_new['l'+key[7:]] = values
+        else:
+            param_dict_new[key] = values
+    return param_dict_new
+

imageProcess/styleTransfer/models.py

@@ -1,3 +0,0 @@
-from django.db import models
-
-# Create your models here.

imageProcess/styleTransfer/train.py

@@ -0,0 +1,77 @@
+# coding=utf-8
+import os
+# import moxing as mox
+import numpy as np
+import cv2 as cv
+import mindspore
+from mindspore import nn, Tensor, Parameter, context
+from mindspore import load_checkpoint, load_param_into_net, save_checkpoint
+from vgg import Vgg19
+from method import create_dataset, load_image, gram_matrix, Optim_Loss, save_img, load_parameters
+# 设置为动态图模式
+import argparse
+# parser = argparse.ArgumentParser(description='Training img transfer')
+# parser.add_argument('--device_target', type=str, default="Ascend", choices=['Ascend', 'GPU', 'CPU'])
+# parser.add_argument('--data_url', required=True, default=None)
+# parser.add_argument('--train_url', required=True, default=None)
+# # parser.add_argument("model_url", type=str, default=None, help="pretrained checkpoint file path of vgg.")
+# args = parser.parse_known_args()[0]
+# context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+
+# if not os.path.exists("./img_data"):
+#     os.mkdir("./img_data")
+#     mox.file.copy_parallel(args.data_url, "./img_data")
+#
+# if not os.path.exists('./output_img'):
+#     os.mkdir("./output_img")
+
+vgg = Vgg19()
+param_dict = load_parameters("vgg19.ckpt")
+print("ckpt load success")
+load_param_into_net(vgg, param_dict)
+print("param load success")
+vgg.set_train(False)   # 评估模式
+
+
+def load_img_ckpt(path):
+    return load_parameters(path)
+
+
+def optimize_picture(content_path, style_path, iter_num, learning_rate, output_path, mode):
+    if mode == 0:
+        optimize_img = load_image(content_path, 400)
+        content_maps = vgg(Tensor(optimize_img))
+        optimizing_param = Parameter(Tensor(optimize_img, mindspore.float32), "optimizing_img")
+    else:
+        optimize_img = load_img_ckpt(content_path)["img"]
+        content_maps = vgg(Tensor(optimize_img))
+        optimizing_param = Parameter(Tensor(optimize_img, mindspore.float32), "optimizing_img")
+    style_img = load_image(style_path, 400)
+    # 特征图提取（6层）
+    content_maps = content_maps[4].squeeze(axis=0)    # 内容图片vgg19提取的特征
+    style_maps = list(vgg(Tensor(style_img)))    # 风格图片的特征
+    style_maps.pop(4)      # 剔除掉不需要的map
+    style_gram = list(gram_matrix(x) for x in style_maps)
+    # 要优化的图片
+
+    num_of_iterations = iter_num + 1    # 迭代的次数
+    loss_opt = Optim_Loss(vgg, [content_maps, style_gram, optimizing_param])
+    cosine_decay_lr = nn.CosineDecayLR(0.05, 2.0, 2000)
+    optimizer_n = nn.Adam([optimizing_param], learning_rate=cosine_decay_lr)  # 损失优化方法
+    # optimizer = nn.Adam([optimizing_param], learning_rate=learning_rate)   # 损失优化方法
+    train_net = nn.TrainOneStepCell(loss_opt, optimizer_n)
+    train_net.set_train()
+    for i in range(num_of_iterations):
+        if i % 50 == 0:
+            save_img(i, optimizer_n, output_path)
+        loss = train_net()
+        print(str(content_path[-13:-4]) + "_to_" + str(style_path[-8:-4]) + ":iteration_", str(i), ":  loss", loss)
+    final = optimizer_n.parameters
+    save_checkpoint([{"name": "img", "data": final[0]}], output_path+"/img_1.ckpt")
+
+
+optimize_picture("output_img/iter_19/img.ckpt",
+                 "transfer_img/trans_6/sty_fam_1.jpg", 2000, 0.05, "output_img/iter_19", 1)
+
+# img = cv.imread("transfer_img/trans_5/style_img.jpg")[:, :, ::-1]
+# print(img.shape)

imageProcess/styleTransfer/vgg.py

@@ -0,0 +1,54 @@
+
+from mindspore import load_checkpoint, load_param_into_net
+from mindspore import nn, context
+
+
+def load_parameters(file_name):
+    param_dict = load_checkpoint(file_name)
+    param_dict_new = {}
+    # print(param_dict)
+    for key, values in param_dict.items():
+        if key.startswith('moments.'):
+            continue
+        elif key.startswith("layers."):
+            param_dict_new['l'+key[7:]] = values
+        else:
+            param_dict_new[key] = values
+    return param_dict_new
+
+
+class Vgg19(nn.Cell):
+    def __init__(self):
+        super().__init__()
+        self.l0 = nn.Conv2d(3, 64, kernel_size=3, weight_init='ones')
+        self.l2 = nn.Conv2d(64, 64, kernel_size=3, weight_init='ones')
+        self.l5 = nn.Conv2d(64, 128, kernel_size=3, weight_init='ones')
+        self.l7 = nn.Conv2d(128, 128, kernel_size=3, weight_init='ones')
+        self.l10 = nn.Conv2d(128, 256, kernel_size=3, weight_init='ones')
+        self.l12 = nn.Conv2d(256, 256, kernel_size=3, weight_init='ones')
+        self.l14 = nn.Conv2d(256, 256, kernel_size=3, weight_init='ones')
+        self.l16 = nn.Conv2d(256, 256, kernel_size=3, weight_init='ones')
+        self.l19 = nn.Conv2d(256, 512, kernel_size=3, weight_init='ones')
+        self.l21 = nn.Conv2d(512, 512, kernel_size=3, weight_init='ones')
+        self.l23 = nn.Conv2d(512, 512, kernel_size=3, weight_init='ones')
+        self.l25 = nn.Conv2d(512, 512, kernel_size=3, weight_init='ones')
+        self.l28 = nn.Conv2d(512, 512, kernel_size=3, weight_init='ones')
+        self.l30 = nn.Conv2d(512, 512, kernel_size=3, weight_init='ones')
+        self.l32 = nn.Conv2d(512, 512, kernel_size=3, weight_init='ones')
+        self.l34 = nn.Conv2d(512, 512, kernel_size=3, weight_init='ones')
+        self.relu = nn.ReLU()
+        self.mp = nn.MaxPool2d(kernel_size=2, stride=2)
+        self.flatten = nn.Flatten()
+
+    def construct(self, x):
+        layer_1 = self.relu(self.l0(x))     # 3-64
+        layer_2 = self.relu(self.l5(self.mp(self.relu(self.l2(layer_1)))))
+        layer_3 = self.relu(self.l10(self.relu(self.l7(self.mp(layer_2)))))
+        layer_4 = self.relu(self.l19(self.mp(self.relu(self.l16(self.relu(self.l14(self.relu(self.l12(layer_3)))))))))
+        layer_4_2 = self.relu(self.l21(layer_4))
+        layer_5 = self.relu(self.l28(self.mp(self.relu(self.l25(self.relu(self.l23(layer_4_2)))))))
+        return [layer_1, layer_2, layer_3, layer_4, layer_4_2, layer_5]
+
+
+
+

imageProcess/styleTransfer/views.py

@@ -1,3 +1,84 @@
-from django.shortcuts import render
+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 = "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
 
-# Create your views here.

imageProcessFront/src/views/Home.vue

@@ -1,6 +1,7 @@
 <template>
   <div>
     <el-container>
+      <el-button type="primary" plain @click="saveImg">保存图片</el-button>
       <el-main style="padding:10px;margin-right:10px;border-radius: 10px;"><router-view :key="$route.fullPath"/>
 
      <el-dialog title="图片上传" :visible.sync="dialogVisible" width="30%" 
@@ -30,22 +31,42 @@
 
       <el-footer>
 
+        <el-dialog title="风格转换" :visible.sync="styleVisible" width="80%">
+        <img src='../assets/candy.png' height="100px" width="100px" class="exampleImage" @click="styleTransfer(0)"/>
+        <img src='../assets/la_muse.png' height="100px" width="100px" class="exampleImage"  @click="styleTransfer(2)"/>
+         <img src='../assets/mosaic.png' height="100px" width="100px" class="exampleImage"  @click="styleTransfer(4)"/>
+        <br>
+        <img v-bind:src="preview" height="300px" width="300px"  /> 
+        <br>
+        <span>
+        <el-button @click="styleVisible = false">取 消</el-button>
+        <el-button type="primary" @click="styleVisible = false;image=preview">确 定</el-button>
+        </span>
+        </el-dialog>
+
         <el-dialog title="尺寸" :visible.sync="sizeVisible" width="30%">
         高度:<el-input-number v-model="tmpHeight" :precision="0"  :min="1" step="1" :max="100000" ></el-input-number><br />
         宽度:<el-input-number v-model="tmpWidth" :precision="0"  :min="1" step="1" :max="100000" ></el-input-number><br />
         <span>
         <el-button @click="sizeVisible = false">取 消</el-button>
-        <el-button type="primary" @click="submitSize">确 定</el-button>
+        <el-button type="primary" @click="submitSize()">确 定</el-button>
         </span>
         </el-dialog>
 
         <el-menu  class="el-menu-demo" mode="horizontal">
-        <el-menu-item index="1"  @click="clickMenu(1)">改变大小</el-menu-item>
+        <el-menu-item index="1"  @click="clickMenu(1)">改变比例</el-menu-item>
         <el-menu-item index="2"  @click="clickMenu(2)">水平镜像</el-menu-item>
         <el-menu-item index="3"  @click="clickMenu(3)">垂直镜像</el-menu-item>
         <el-menu-item index="4"  @click="clickMenu(4)">对角镜像</el-menu-item>
-        <el-menu-item index="4"  @click="clickMenu(5)">向左旋转</el-menu-item>
-        <el-menu-item index="4"  @click="clickMenu(6)">向右旋转</el-menu-item>
+        <el-menu-item index="5"  @click="clickMenu(5)">向左旋转</el-menu-item>
+        <el-menu-item index="6"  @click="clickMenu(6)">向右旋转</el-menu-item>
+        <el-menu-item index="7"  @click="clickMenu(7)">抠图</el-menu-item>
+        <el-menu-item index="8"  @click="clickMenu(8)">风格转换</el-menu-item>
+        <el-menu-item index="9"  @click="clickMenu(9)">图像平滑</el-menu-item>
+        <el-menu-item index="10"  @click="clickMenu(10)">锐化</el-menu-item>
+        <el-menu-item index="11"  @click="clickMenu(11)">曝光调整</el-menu-item>
+        <el-menu-item index="12"  @click="clickMenu(12)">条形码识别</el-menu-item>
+        <el-menu-item index="13"  @click="clickMenu(13)">去噪</el-menu-item>
         </el-menu>
       </el-footer>
     </el-container>
@@ -111,6 +132,7 @@ import axios from "axios"
 export default {
    data() {
       return {
+        styleVisible: false,
         dialogVisible: true,
         sizeVisible: false,
         imageUrls: [
@@ -118,12 +140,17 @@ export default {
           'https://fuss10.elemecdn.com/1/34/19aa98b1fcb2781c4fba33d850549jpeg.jpeg',
           'https://fuss10.elemecdn.com/0/6f/e35ff375812e6b0020b6b4e8f9583jpeg.jpeg',
         ],
+        styleUrls: [
+          '../assets/candy.png',
+        ],
         image: "",
+        preview: "",
         heightInput: "",
         widthInput:"",
         tmpHeight:"",
         tmpWidth:"",
         fileList: [],
+        str: "https://huashi-1305159828.cos.ap-shanghai.myqcloud.com/202110269108S.%E5%8A%B3%E5%8A%A8%E6%AD%A3%E4%B9%89%EF%BC%9A%E5%8A%B3%E5%8A%A8%E5%B9%B8%E7%A6%8F%E4%B8%8D%E5%8F%AF%E6%88%96%E7%BC%BA%E7%9A%84%E4%BB%B7%E5%80%BC%E6%94%AF%E6%92%91_%E6%AF%9B%E5%8B%92%E5%A0%82.pdf"
       }
     },
     methods: {
@@ -184,6 +211,42 @@ export default {
         formdata.append("direction",'right')
         axios.post("/rotate/", formdata).then(res => { var result=res.data;this.image="data:image/png;base64," + result.image; })
       }
+      else if(menuIndex==7){
+        let formdata = new FormData()
+        formdata.append("files",this.image)
+        axios.post("/keying/", formdata).then(res => { var result=res.data;this.image="data:image/png;base64," + result.image; })
+      }
+      else if(menuIndex==8){
+        this.styleVisible=true;
+        this.preview=this.image;
+      }
+      else if(menuIndex==9){
+        let formdata = new FormData()
+        formdata.append("files",this.image)
+        axios.post("/smooth/", formdata).then(res => { var result=res.data;this.image="data:image/png;base64," + result.image; })
+      }
+      else if(menuIndex==10){
+        let formdata = new FormData()
+        formdata.append("files",this.image)
+        axios.post("/sharpen/", formdata).then(res => { var result=res.data;this.image="data:image/png;base64," + result.image; })
+      }
+      else if(menuIndex==11){
+        let formdata = new FormData()
+        formdata.append("files",this.image)
+        axios.post("/exposure/", formdata).then(res => { var result=res.data;this.image="data:image/png;base64," + result.image; })
+      }
+       else if(menuIndex==12){
+         let formdata = new FormData()
+        formdata.append("files",this.image)
+        axios.post("/barCode/", formdata).then(res => { var result=res.data;this.image="data:image/png;base64," + result.image; })
+      }
+       else if(menuIndex==13){
+         let formdata = new FormData()
+        formdata.append("files",this.image)
+        axios.post("/removeNoise/", formdata).then(res => { var result=res.data;this.image="data:image/png;base64," + result.image; })
+      }
+      
+      
     },
     submitSize(){
       let formdata = new FormData()
@@ -193,7 +256,31 @@ export default {
       this.sizeVisible=false;
       axios.post("/resize/", formdata).then(res => { var result=res.data;this.image="data:image/png;base64," + result.image;
       this.tmpHeight=result.height;this.tmpWidth=result.width; })
-    }
+    },
+    styleTransfer(index){
+      let formdata = new FormData()
+      formdata.append("files",this.image)
+      formdata.append("style",index)
+      axios.post("/styleTransfer/", formdata).then(res => { var result=res.data;this.preview="data:image/png;base64," + result.image; })
+    },
+    saveImg() {
+      var base64 = this.image;
+      var byteCharacters = atob(
+        base64.replace(/^data:image\/(png|jpeg|jpg);base64,/, "")
+      );
+      var byteNumbers = new Array(byteCharacters.length);
+      for (var i = 0; i < byteCharacters.length; i++) {
+        byteNumbers[i] = byteCharacters.charCodeAt(i);
+      }
+      var byteArray = new Uint8Array(byteNumbers);
+      var blob = new Blob([byteArray], {
+        type: undefined,
+      });
+      var aLink = document.createElement("a");
+      aLink.download = "zy&klx图片在线处理.jpg"; //这里写保存时的图片名称
+      aLink.href = URL.createObjectURL(blob);
+      aLink.click();
+    },
     },
     mounted() {
      },