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| import os | |
| import sys | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| from torchvision import transforms | |
| from PIL import Image | |
| from network import pvt_cls as TCN | |
| import gradio as gr | |
| def demo(img_path): | |
| # config | |
| batch_size = 8 | |
| crop_size = 256 | |
| model_path = './models/best_model.pth' | |
| device = torch.device('cuda') | |
| # prepare model | |
| model = TCN.pvt_treeformer(pretrained=False) | |
| model.to(device) | |
| model.load_state_dict(torch.load(model_path, device)) | |
| model.eval() | |
| # preprocess | |
| img = Image.open(img_path).convert('RGB') | |
| show_img = np.array(img) | |
| wd, ht = img.size | |
| st_size = 1.0 * min(wd, ht) | |
| if st_size < crop_size: | |
| rr = 1.0 * crop_size / st_size | |
| wd = round(wd * rr) | |
| ht = round(ht * rr) | |
| st_size = 1.0 * min(wd, ht) | |
| img = img.resize((wd, ht), Image.BICUBIC) | |
| transform = transforms.Compose([ | |
| transforms.ToTensor(), | |
| transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]) | |
| ]) | |
| img = transform(img) | |
| img = img.unsqueeze(0) | |
| # model forward | |
| with torch.no_grad(): | |
| inputs = img.to(device) | |
| crop_imgs, crop_masks = [], [] | |
| b, c, h, w = inputs.size() | |
| rh, rw = crop_size, crop_size | |
| for i in range(0, h, rh): | |
| gis, gie = max(min(h - rh, i), 0), min(h, i + rh) | |
| for j in range(0, w, rw): | |
| gjs, gje = max(min(w - rw, j), 0), min(w, j + rw) | |
| crop_imgs.append(inputs[:, :, gis:gie, gjs:gje]) | |
| mask = torch.zeros([b, 1, h, w]).to(device) | |
| mask[:, :, gis:gie, gjs:gje].fill_(1.0) | |
| crop_masks.append(mask) | |
| crop_imgs, crop_masks = map(lambda x: torch.cat( | |
| x, dim=0), (crop_imgs, crop_masks)) | |
| crop_preds = [] | |
| nz, bz = crop_imgs.size(0), batch_size | |
| for i in range(0, nz, bz): | |
| gs, gt = i, min(nz, i + bz) | |
| crop_pred, _ = model(crop_imgs[gs:gt]) | |
| crop_pred = crop_pred[0] | |
| _, _, h1, w1 = crop_pred.size() | |
| crop_pred = F.interpolate(crop_pred, size=( | |
| h1 * 4, w1 * 4), mode='bilinear', align_corners=True) / 16 | |
| crop_preds.append(crop_pred) | |
| crop_preds = torch.cat(crop_preds, dim=0) | |
| # splice them to the original size | |
| idx = 0 | |
| pred_map = torch.zeros([b, 1, h, w]).to(device) | |
| for i in range(0, h, rh): | |
| gis, gie = max(min(h - rh, i), 0), min(h, i + rh) | |
| for j in range(0, w, rw): | |
| gjs, gje = max(min(w - rw, j), 0), min(w, j + rw) | |
| pred_map[:, :, gis:gie, gjs:gje] += crop_preds[idx] | |
| idx += 1 | |
| # for the overlapping area, compute average value | |
| mask = crop_masks.sum(dim=0).unsqueeze(0) | |
| outputs = pred_map / mask | |
| outputs = F.interpolate(outputs, size=( | |
| h, w), mode='bilinear', align_corners=True)/4 | |
| outputs = pred_map / mask | |
| model_output = round(torch.sum(outputs).item()) | |
| print("{}: {}".format(img_path, model_output)) | |
| outputs = outputs.squeeze().cpu().numpy() | |
| outputs = (outputs - np.min(outputs)) / \ | |
| (np.max(outputs) - np.min(outputs)) | |
| show_img = show_img / 255.0 | |
| show_img = show_img * 0.2 + outputs[:, :, None] * 0.8 | |
| return model_output, show_img | |
| if __name__ == "__main__": | |
| # test | |
| # img_path = sys.argv[1] | |
| # demo(img) | |
| # Launch a gr.Interface | |
| gr_demo = gr.Interface(fn=demo, | |
| inputs=gr.Image(sources=["upload", "webcam", "clipboard"], | |
| type="filepath", | |
| label="Input Image", | |
| width=768, | |
| height=768, | |
| ), | |
| outputs=[ | |
| gr.Number(label="Predicted Tree Count"), | |
| gr.Image(label="Density Map", | |
| width=768, | |
| height=768, | |
| ) | |
| ], | |
| title="TreeFormer", | |
| description="TreeFormer is a semi-supervised transformer-based framework for tree counting from a single high resolution image. Upload an image and TreeFormer will predict the number of trees in the image and generate a density map of the trees.", | |
| article="This work has been developed a spart of the ReSET project which has received funding from the European Union's Horizon 2020 FET Proactive Programme under grant agreement No 101017857. The contents of this publication are the sole responsibility of the ReSET consortium and do not necessarily reflect the opinion of the European Union.", | |
| examples=[ | |
| ["./examples/IMG_101.jpg"], | |
| ["./examples/IMG_125.jpg"], | |
| ["./examples/IMG_138.jpg"], | |
| ["./examples/IMG_180.jpg"], | |
| ["./examples/IMG_18.jpg"], | |
| ["./examples/IMG_206.jpg"], | |
| ["./examples/IMG_223.jpg"], | |
| ["./examples/IMG_247.jpg"], | |
| ["./examples/IMG_270.jpg"], | |
| ["./examples/IMG_306.jpg"], | |
| ], | |
| # cache_examples=True, | |
| examples_per_page=10, | |
| allow_flagging=False, | |
| theme=gr.themes.Default(), | |
| ) | |
| gr_demo.launch(share=True, server_port=7860, favicon_path="./assets/reset.png") | |