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| import h5py | |
| import gradio as gr | |
| import scipy.io as io | |
| import PIL.Image as Image | |
| import numpy as np | |
| from torchvision import transforms | |
| import scipy | |
| import json | |
| from matplotlib import cm as CM | |
| import torch.nn as nn | |
| import torch | |
| from torchvision import models | |
| class CSRNet(nn.Module): | |
| def __init__(self, load_weights=False): | |
| super(CSRNet, self).__init__() | |
| self.seen = 0 | |
| self.frontend_feat = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512] | |
| self.backend_feat = [512, 512, 512, 256, 128, 64] | |
| self.frontend = make_layers(self.frontend_feat) | |
| self.backend = make_layers(self.backend_feat, in_channels=512, dilation=True) | |
| self.output_layer = nn.Conv2d(64, 1, kernel_size=1) | |
| if not load_weights: | |
| mod = models.vgg16(pretrained=True) | |
| self._initialize_weights() | |
| mod_dict = mod.state_dict() | |
| frontend_dict = self.frontend.state_dict() | |
| for k, v in mod_dict.items(): | |
| if k in frontend_dict: | |
| frontend_dict[k].data = v.data | |
| def forward(self,x): | |
| x = self.frontend(x) | |
| x = self.backend(x) | |
| x = self.output_layer(x) | |
| return x | |
| def _initialize_weights(self): | |
| for m in self.modules(): | |
| if isinstance(m, nn.Conv2d): | |
| nn.init.normal_(m.weight, std=0.01) | |
| if m.bias is not None: | |
| nn.init.constant_(m.bias, 0) | |
| elif isinstance(m, nn.BatchNorm2d): | |
| nn.init.constant_(m.weight, 1) | |
| nn.init.constant_(m.bias, 0) | |
| def make_layers(cfg, in_channels = 3,batch_norm=False,dilation = False): | |
| if dilation: | |
| d_rate = 2 | |
| else: | |
| d_rate = 1 | |
| layers = [] | |
| for v in cfg: | |
| if v == 'M': | |
| layers += [nn.MaxPool2d(kernel_size=2, stride=2)] | |
| else: | |
| conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=d_rate,dilation = d_rate) | |
| if batch_norm: | |
| layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] | |
| else: | |
| layers += [conv2d, nn.ReLU(inplace=True)] | |
| in_channels = v | |
| return nn.Sequential(*layers) | |
| # Load the CSRNet model | |
| csrmodel = CSRNet() | |
| checkpoint = torch.load("model.pt") | |
| csrmodel.load_state_dict(checkpoint) | |
| csrmodel.eval() | |
| # Set the transformation for image preprocessing | |
| transform = transforms.Compose([ | |
| transforms.ToPILImage(), | |
| transforms.Resize((256, 256)), | |
| transforms.ToTensor(), | |
| transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| ]) | |
| # Define the prediction function | |
| def predict_count(input_image): | |
| # Preprocess the input image | |
| image = transform(input_image).unsqueeze(0) | |
| # Perform the forward pass | |
| output = csrmodel(image) | |
| # Calculate the predicted count | |
| predicted_count = int(output.detach().cpu().sum().numpy()) | |
| return predicted_count | |
| # Define the input and output interfaces for Gradio | |
| input_interface = gr.inputs.Image() | |
| output_interface = gr.outputs.Textbox() | |
| # Create the Gradio app | |
| grapp = gr.Interface(fn=predict_count, inputs=input_interface, outputs=output_interface) | |
| # Launch the app | |
| grapp.launch() |