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import numpy
import torch
import gradio as gr
from einops import rearrange
from torchvision import transforms
from model import CANNet
model = CANNet()
checkpoint = torch.load('part_B_pre.pth.tar',map_location=torch.device('cpu'))
model.load_state_dict(checkpoint['state_dict'])
model.eval()
## Defining the transform function
transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize(mean=[0.485, 0.456, 0.406],std=[0.229, 0.224, 0.225])])
def crowd(img):
## Transforming the image
img = transform(img)
## Adding batch dimension
img = rearrange(img, "c h w -> 1 c h w")
## Slicing the image into four parts
h = img.shape[2]
w = img.shape[3]
h_d = int(h/2)
w_d = int(w/2)
img_1 = img[:,:,:h_d,:w_d]
img_2 = img[:,:,:h_d,w_d:]
img_3 = img[:,:,h_d:,:w_d]
img_4 = img[:,:,h_d:,w_d:]
## Inputting the 4 images into the model, converting it to numpy array, and summing to get the density
with torch.no_grad():
density_1 = model(img_1).numpy().sum()
density_2 = model(img_2).numpy().sum()
density_3 = model(img_3).numpy().sum()
density_4 = model(img_4).numpy().sum()
## Summing up the estimated density and rounding the result to get an integer
pred = density_1 + density_2 + density_3 + density_4
pred = int(pred.round())
return pred
outputs = gr.outputs.Textbox(type="text", label="Estimated crowd density:")
inputs = gr.inputs.Image(type="numpy", label="Input the image here:")
gr.Interface(fn=crowd, inputs=inputs, outputs=outputs, allow_flagging="never").launch(inbrowser=True) |