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app (29).py

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+import numpy as np
+import torch
+import torch.nn as nn
+import gradio as gr
+from PIL import Image
+import torchvision.transforms as transforms
+import os  # 📁 For file operations
+
+# 🧠 Neural network layers
+norm_layer = nn.InstanceNorm2d
+
+# 🧱 Building block for the generator
+class ResidualBlock(nn.Module):
+    def __init__(self, in_features):
+        super(ResidualBlock, self).__init__()
+
+        conv_block = [  nn.ReflectionPad2d(1),
+                        nn.Conv2d(in_features, in_features, 3),
+                        norm_layer(in_features),
+                        nn.ReLU(inplace=True),
+                        nn.ReflectionPad2d(1),
+                        nn.Conv2d(in_features, in_features, 3),
+                        norm_layer(in_features)
+                        ]
+
+        self.conv_block = nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        return x + self.conv_block(x)
+
+# 🎨 Generator model for creating line drawings
+class Generator(nn.Module):
+    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
+        super(Generator, self).__init__()
+
+        # 🏁 Initial convolution block
+        model0 = [   nn.ReflectionPad2d(3),
+                    nn.Conv2d(input_nc, 64, 7),
+                    norm_layer(64),
+                    nn.ReLU(inplace=True) ]
+        self.model0 = nn.Sequential(*model0)
+
+        # 🔽 Downsampling
+        model1 = []
+        in_features = 64
+        out_features = in_features*2
+        for _ in range(2):
+            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
+                        norm_layer(out_features),
+                        nn.ReLU(inplace=True) ]
+            in_features = out_features
+            out_features = in_features*2
+        self.model1 = nn.Sequential(*model1)
+
+        # 🔁 Residual blocks
+        model2 = []
+        for _ in range(n_residual_blocks):
+            model2 += [ResidualBlock(in_features)]
+        self.model2 = nn.Sequential(*model2)
+
+        # 🔼 Upsampling
+        model3 = []
+        out_features = in_features//2
+        for _ in range(2):
+            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
+                        norm_layer(out_features),
+                        nn.ReLU(inplace=True) ]
+            in_features = out_features
+            out_features = in_features//2
+        self.model3 = nn.Sequential(*model3)
+
+        # 🎭 Output layer
+        model4 = [  nn.ReflectionPad2d(3),
+                        nn.Conv2d(64, output_nc, 7)]
+        if sigmoid:
+            model4 += [nn.Sigmoid()]
+
+        self.model4 = nn.Sequential(*model4)
+
+    def forward(self, x, cond=None):
+        out = self.model0(x)
+        out = self.model1(out)
+        out = self.model2(out)
+        out = self.model3(out)
+        out = self.model4(out)
+
+        return out
+
+# 🔧 Load the models
+model1 = Generator(3, 1, 3)
+model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu'), weights_only=True))
+model1.eval()
+
+model2 = Generator(3, 1, 3)
+model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu'), weights_only=True))
+model2.eval()
+
+# 🖼️ Function to process the image and create line drawing
+def predict(input_img, ver):
+    # Open the image and get its original size
+    original_img = Image.open(input_img)
+    original_size = original_img.size
+
+    # Define the transformation pipeline
+    transform = transforms.Compose([
+        transforms.Resize(256, Image.BICUBIC),
+        transforms.ToTensor(),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
+    ])
+
+    # Apply the transformation
+    input_tensor = transform(original_img)
+    input_tensor = input_tensor.unsqueeze(0)
+
+    # Process the image through the model
+    with torch.no_grad():
+        if ver == 'Simple Lines':
+            output = model2(input_tensor)
+        else:
+            output = model1(input_tensor)
+
+    # Convert the output tensor to an image
+    output_img = transforms.ToPILImage()(output.squeeze().cpu().clamp(0, 1))
+
+    # Resize the output image back to the original size
+    output_img = output_img.resize(original_size, Image.BICUBIC)
+
+    return output_img
+
+# 📝 Title for the Gradio interface
+title="🖌️ Image to Line Drawings - Complex and Simple Portraits and Landscapes"
+
+# 🖼️ Dynamically generate examples from images in the directory
+examples = []
+image_dir = '.'  # Assuming images are in the current directory
+for file in os.listdir(image_dir):
+    if file.lower().endswith(('.png', '.jpg', '.jpeg', '.gif')):
+        examples.append([file, 'Simple Lines'])
+        examples.append([file, 'Complex Lines'])
+
+# 🚀 Create and launch the Gradio interface
+iface = gr.Interface(
+    fn=predict, 
+    inputs=[
+        gr.Image(type='filepath'),
+        gr.Radio(['Complex Lines', 'Simple Lines'], label='version', value='Simple Lines')
+    ],
+    outputs=gr.Image(type="pil"),
+    title=title,
+    examples=examples
+)
+
+iface.launch()

backup1.app.py

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+import numpy as np
+import torch
+import torch.nn as nn
+import gradio as gr
+from PIL import Image
+import torchvision.transforms as transforms
+
+norm_layer = nn.InstanceNorm2d
+
+class ResidualBlock(nn.Module):
+    def __init__(self, in_features):
+        super(ResidualBlock, self).__init__()
+
+        conv_block = [  nn.ReflectionPad2d(1),
+                        nn.Conv2d(in_features, in_features, 3),
+                        norm_layer(in_features),
+                        nn.ReLU(inplace=True),
+                        nn.ReflectionPad2d(1),
+                        nn.Conv2d(in_features, in_features, 3),
+                        norm_layer(in_features)
+                        ]
+
+        self.conv_block = nn.Sequential(*conv_block)
+
+    def forward(self, x):
+        return x + self.conv_block(x)
+
+
+class Generator(nn.Module):
+    def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
+        super(Generator, self).__init__()
+
+        # Initial convolution block
+        model0 = [   nn.ReflectionPad2d(3),
+                    nn.Conv2d(input_nc, 64, 7),
+                    norm_layer(64),
+                    nn.ReLU(inplace=True) ]
+        self.model0 = nn.Sequential(*model0)
+
+        # Downsampling
+        model1 = []
+        in_features = 64
+        out_features = in_features*2
+        for _ in range(2):
+            model1 += [  nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
+                        norm_layer(out_features),
+                        nn.ReLU(inplace=True) ]
+            in_features = out_features
+            out_features = in_features*2
+        self.model1 = nn.Sequential(*model1)
+
+        model2 = []
+        # Residual blocks
+        for _ in range(n_residual_blocks):
+            model2 += [ResidualBlock(in_features)]
+        self.model2 = nn.Sequential(*model2)
+
+        # Upsampling
+        model3 = []
+        out_features = in_features//2
+        for _ in range(2):
+            model3 += [  nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
+                        norm_layer(out_features),
+                        nn.ReLU(inplace=True) ]
+            in_features = out_features
+            out_features = in_features//2
+        self.model3 = nn.Sequential(*model3)
+
+        # Output layer
+        model4 = [  nn.ReflectionPad2d(3),
+                        nn.Conv2d(64, output_nc, 7)]
+        if sigmoid:
+            model4 += [nn.Sigmoid()]
+
+        self.model4 = nn.Sequential(*model4)
+
+    def forward(self, x, cond=None):
+        out = self.model0(x)
+        out = self.model1(out)
+        out = self.model2(out)
+        out = self.model3(out)
+        out = self.model4(out)
+
+        return out
+
+model1 = Generator(3, 1, 3)
+model1.load_state_dict(torch.load('model.pth', map_location=torch.device('cpu')))
+model1.eval()
+
+model2 = Generator(3, 1, 3)
+model2.load_state_dict(torch.load('model2.pth', map_location=torch.device('cpu')))
+model2.eval()
+
+def predict(input_img, ver):
+    input_img = Image.open(input_img)
+    transform = transforms.Compose([transforms.Resize(256, Image.BICUBIC), transforms.ToTensor()])
+    input_img = transform(input_img)
+    input_img = torch.unsqueeze(input_img, 0)
+
+    drawing = 0
+    with torch.no_grad():
+        if ver == 'Simple Lines':
+            drawing = model2(input_img)[0].detach()
+        else:
+            drawing = model1(input_img)[0].detach()
+    
+    drawing = transforms.ToPILImage()(drawing)
+    return drawing
+
+title="Image to Line Drawings - Complex and Simple Portraits and Landscapes"
+examples=[
+['01.jpeg', 'Simple Lines'], ['02.jpeg', 'Simple Lines'], ['03.jpeg', 'Simple Lines'],
+['07.jpeg', 'Complex Lines'], ['08.jpeg', 'Complex Lines'], ['09.jpeg', 'Complex Lines'],
+['10.jpeg', 'Simple Lines'], ['11.jpeg', 'Simple Lines'], ['12.jpeg', 'Simple Lines'],
+['01.jpeg', 'Complex Lines'], ['02.jpeg', 'Complex Lines'], ['03.jpeg', 'Complex Lines'],
+['04.jpeg', 'Simple Lines'], ['05.jpeg', 'Simple Lines'], ['06.jpeg', 'Simple Lines'],
+['07.jpeg', 'Simple Lines'], ['08.jpeg', 'Simple Lines'], ['09.jpeg', 'Simple Lines'],
+['04.jpeg', 'Complex Lines'], ['05.jpeg', 'Complex Lines'], ['06.jpeg', 'Complex Lines'],
+['10.jpeg', 'Complex Lines'], ['11.jpeg', 'Complex Lines'], ['12.jpeg', 'Complex Lines'],
+['Upload Wild Horses 2.jpeg', 'Complex Lines']
+]
+
+iface = gr.Interface(predict, [gr.inputs.Image(type='filepath'),
+    gr.inputs.Radio(['Complex Lines','Simple Lines'], type="value", default='Simple Lines', label='version')],
+    gr.outputs.Image(type="pil"), title=title,examples=examples)
+
+iface.launch()

model.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c686ced2a666b4850b4bb6ccf0748031c3eda9f822de73a34b8979970d90f0c6
+size 17173511

model2.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:30a534781061f34e83bb9406b4335da4ff2616c95d22a585c1245aa8363e74e0
+size 17173511

requirements (11).txt

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+torch
+torchvision