添加了关于内容

modified:   helloworld.py
	modified:   nst/Models.py
	new file:   nst/helloworld.py
	modified:   sub_windows/ui_sub_window_10.py

helloworld.py

@@ -1,4 +1,6 @@
 import cv2
+
+
 def detect_cameras(max_cameras=10):
     cameras = []
     for i in range(max_cameras):
@@ -10,7 +12,10 @@ def detect_cameras(max_cameras=10):
             cap.release()
     return cameras
 
+
 # available_cameras = detect_cameras()
 # print(f'Found {len(available_cameras)} camera(s): {available_cameras}'
 
-print(cv2.__version__)
+image = cv2.imread("nst/ori/1.jpg")
+cv2.imshow("Image", image)
+cv2.waitKey(0)

nst/Models.py

@@ -13,6 +13,7 @@ from tools import Tool
 
 tl = Tool()
 
+
 class ContentLoss(nn.Module):
     """内容损失"""
 
@@ -68,24 +69,25 @@ class StyleLoss(ContentLoss):
 
 
 class Transfer(object):
-    def __init__(self, fn_content, fn_style, model_path=r'weights/squeezenet1_0-a815701f.pth'):
+    def __init__(
+        self, fn_content, fn_style, model_path=r"weights/squeezenet1_0-a815701f.pth"
+    ):
         """usage:
-            net = Transfer('picasso.jpg','dancing.jpg')
-            dt, img = net.fit()
-            """
+        net = Transfer('picasso.jpg','dancing.jpg')
+        dt, img = net.fit()
+        """
         self.use_cuda, dtype, imsize = tl.config()
 
         self.content_img = tl.image_loader(fn_content).type(dtype)
         self.style_img = tl.image_loader(fn_style).type(dtype)
         self.input_img = self.content_img.clone()
 
-
         """
         get_style_model_and_losses函数是针对vgg模型的,
         要应用到其他模型,需要改写该函数;"""
-        if 'vgg19' in model_path:
+        if "vgg19" in model_path:
             self.seq = self.load_vgg19(model_path)
-        elif 'resnet18' in model_path:
+        elif "resnet18" in model_path:
             self.seq = self.load_resnet18(model_path)
         elif "squeezenet1_0" in model_path:
             self.seq = self.load_squeezenet(model_path, "1_0")
@@ -104,7 +106,6 @@ class Transfer(object):
         cnn.load_state_dict(torch.load(model_path))
         return cnn.features[:23]
 
-
     def load_squeezenet(self, model_path, version):
         """加载SqueezeNet1.0预训练模型;"""
         model = models.SqueezeNet(version=version)
@@ -113,10 +114,12 @@ class Transfer(object):
 
     def load_densenet(self, model_path):
         """加载densenet121预训练模型;"""
-        model = models.DenseNet(num_init_features=64, growth_rate=32,
-                                block_config=(6, 12, 24, 16))
+        model = models.DenseNet(
+            num_init_features=64, growth_rate=32, block_config=(6, 12, 24, 16)
+        )
         pattern = re.compile(
-            r'^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$')
+            r"^(.*denselayer\d+\.(?:norm|relu|conv))\.((?:[12])\.(?:weight|bias|running_mean|running_var))$"
+        )
         state_dict = torch.load(model_path)
         for key in list(state_dict.keys()):
             res = pattern.match(key)
@@ -146,19 +149,33 @@ class Transfer(object):
         outout_img:PIL.Image.Image;
         style_weight需要远远大于content_weight;"""
         t0 = time.time()
-        cnn, tensor = self.rebuild(self.seq, self.content_img,
-                                   self.style_img, self.input_img,
-                                   num_steps, content_weight,
-                                   style_weight)
+        cnn, tensor = self.rebuild(
+            self.seq,
+            self.content_img,
+            self.style_img,
+            self.input_img,
+            num_steps,
+            content_weight,
+            style_weight,
+        )
         output_img = tl.batch_tensor2pil(tensor)
         dt = time.time() - t0
         return dt, output_img
 
-    def rebuild(self, cnn, content_img, style_img, input_img, num_steps,
-                content_weight, style_weight):
+    def rebuild(
+        self,
+        cnn,
+        content_img,
+        style_img,
+        input_img,
+        num_steps,
+        content_weight,
+        style_weight,
+    ):
         """run the style transfer."""
-        model, style_losses, content_losses = self.get_losses(cnn, style_img, content_img,
-                                                              style_weight, content_weight)
+        model, style_losses, content_losses = self.get_losses(
+            cnn, style_img, content_img, style_weight, content_weight
+        )
         input_param, optimizer = self.get_optimizer(input_img)
 
         run = [0]
@@ -180,8 +197,11 @@ class Transfer(object):
 
                 run[0] += 1
                 if run[0] % 50 == 0:
-                    print('Style Loss : {:4f} Content Loss: {:4f}'.format(style_score, content_score))
-
+                    print(
+                        "Style Loss : {:4f} Content Loss: {:4f}".format(
+                            style_score, content_score
+                        )
+                    )
 
                 return style_score + content_score
 
@@ -192,10 +212,16 @@ class Transfer(object):
 
         return model, input_param.data
 
-    def get_losses(self, cnn, style_img, content_img,
-                   style_weight, content_weight,
-                   content_layers=['conv_4'],
-                   style_layers=['conv_1', 'conv_2', 'conv_3', 'conv_4', 'conv_5']):
+    def get_losses(
+        self,
+        cnn,
+        style_img,
+        content_img,
+        style_weight,
+        content_weight,
+        content_layers=["conv_4"],
+        style_layers=["conv_1", "conv_2", "conv_3", "conv_4", "conv_5"],
+    ):
         cnn = copy.deepcopy(cnn)
 
         # 仅为了有一个可迭代的列表 内容/风格 损失
@@ -264,21 +290,22 @@ class Transfer(object):
         input_param = nn.Parameter(input_img.data)
         optimizer = optim.LBFGS([input_param])
         return input_param, optimizer
-if __name__ == '__main__':
 
+
+if __name__ == "__main__":
     # model = models.vgg19(pretrained=True)
-    ph = 'weights/vgg19-dcbb9e9d.pth'
+    ph = "weights/vgg19-dcbb9e9d.pth"
 
-    transfer = Transfer('ori/2.jpg', 'art/2.jpg',ph)
+    transfer = Transfer("./ori/2.jpg", "./art/2.jpg", ph)
 
     t = time.time()
     dt, img = transfer.fit()
 
-    print(time.time()-t)
+    print(time.time() - t)
     # print(dt,img)
 
-    img = np.array(img)[:,:,::-1]
+    img = np.array(img)[:, :, ::-1]
 
-    cv2.imwrite('1.jpg',img)
-    cv2.imshow('1',img)
+    cv2.imwrite("1.jpg", img)
+    cv2.imshow("1", img)
     cv2.waitKey(0)

nst/helloworld.py

@@ -0,0 +1,5 @@
+import cv2
+
+image = cv2.imread("ori/1.jpg")
+cv2.imshow("Image", image)
+cv2.waitKey(0)

sub_windows/ui_sub_window_10.py

@@ -42,13 +42,13 @@ class Ui_Form(object):
     def retranslateUi(self, Form):
         _translate = QtCore.QCoreApplication.translate
         Form.setWindowTitle(_translate("Form", "关于本软件"))
-        self.label.setText(_translate("Form", "1561130423-阳旭 的毕业设计"))
+        self.label.setText(_translate("Form", "作者：10215101526-许青阳"))
         self.textBrowser.setHtml(_translate("Form", "<!DOCTYPE HTML PUBLIC \"-//W3C//DTD HTML 4.0//EN\" \"http://www.w3.org/TR/REC-html40/strict.dtd\">\n"
 "<html><head><meta name=\"qrichtext\" content=\"1\" /><style type=\"text/css\">\n"
 "p, li { white-space: pre-wrap; }\n"
 "</style></head><body style=\" font-family:\'黑体\'; font-size:10pt; font-weight:400; font-style:normal;\">\n"
-"<p align=\"justify\" style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'宋体\';\">本课题针对图像处理的难点，设计了一款界面友好的图像处理仿真系统，意在帮助初学者形象的理解图像处理所涉及的部分知识点。</span></p>\n"
+"<p align=\"justify\" style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'宋体\';\">本数字图像处理系统针对图像处理的难点，设计了一款界面友好的图像处理仿真系统，意在帮助初学者形象的理解图像处理所涉及的部分知识点。</span></p>\n"
 "<p align=\"justify\" style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'宋体\';\">该软件包含常见的图像处理实例，例如图像缩放、加噪、平滑锐化、直方图均衡、压缩编码，边缘检测、人脸检测等。这些模块涉及到较多图像处理知识，通过每个模块设计的实例，循序渐进的让初学者加深理解图像处理方法间的联系，激发初学者对图像处理的兴趣。</span></p>\n"
 "<p align=\"justify\" style=\" margin-top:12px; margin-bottom:12px; margin-left:0px; margin-right:0px; -qt-block-indent:0; text-indent:0px;\"><span style=\" font-family:\'宋体\';\">本设计利用</span><span style=\" font-family:\'Times New Roman,serif\';\">PyCharm</span><span style=\" font-family:\'宋体\';\">和</span><span style=\" font-family:\'Times New Roman,serif\';\">QT Designer</span><span style=\" font-family:\'宋体\';\">开发平台，基于</span><span style=\" font-family:\'Times New Roman,serif\';\">Python</span><span style=\" font-family:\'宋体\';\">编程语言以及</span><span style=\" font-family:\'Times New Roman,serif\';\">OpenCV</span><span style=\" font-family:\'宋体\';\">计算机视觉库，利用</span><span style=\" font-family:\'Times New Roman,serif\';\">Qt5</span><span style=\" font-family:\'宋体\';\">作为</span><span style=\" font-family:\'Times New Roman,serif\';\">GUI</span><span style=\" font-family:\'宋体\';\">框架，构建了一个兼容</span><span style=\" font-family:\'Times New Roman,serif\';\">Windows-x86</span><span style=\" font-family:\'宋体\';\">平台的软件。</span></p></body></html>"))
-        self.label_2.setText(_translate("Form", "2019-06-03"))
+        self.label_2.setText(_translate("Form", "2023-07-02"))