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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 Artistic Drawing"
# 🖼️ 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() |