完成边缘检测

basic/urls.py

@@ -16,4 +16,5 @@ urlpatterns = [
     path('affine', views.affine),
     path('histogram', views.histogram),
     path('plt', views.piecewise_linear_transform),
+    path('ed', views.edge_detection),
 ]

basic/views.py

@@ -487,6 +487,75 @@ def piecewise_linear_transform(request):
         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')
 
 def r(request):
     return render(request, 'upload.html')