app.py

import argparse
import requests
import gradio as gr
import numpy as np
import cv2
import torch
import torch.nn as nn
from PIL import Image
import torchvision
from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
from timm.data import create_transform

from timmvit import timmvit
import json
from timm.models.hub import download_cached_file
from PIL import Image

def pil_loader(filepath):
    with Image.open(filepath) as img:
        img = img.convert('RGB')
    return img

def build_transforms(input_size, center_crop=True):
    transform = torchvision.transforms.Compose([
        torchvision.transforms.ToPILImage(),
        torchvision.transforms.Resize(input_size * 8 // 7),
        torchvision.transforms.CenterCrop(input_size),
        torchvision.transforms.ToTensor(),
        torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
             ])
    return transform

# Download human-readable labels for Bamboo.
with open('./trainid2name.json') as f:
    id2name = json.load(f)


'''
build model
'''
model = timmvit(pretrain_path='./Bamboo_v0-1_ViT-B16.pth.tar.convert')
model.eval()

'''
borrow code from here: https://github.com/jacobgil/pytorch-grad-cam/blob/master/pytorch_grad_cam/utils/image.py
'''
def show_cam_on_image(img: np.ndarray,
                      mask: np.ndarray,
                      use_rgb: bool = False,
                      colormap: int = cv2.COLORMAP_JET) -> np.ndarray:
    """ This function overlays the cam mask on the image as an heatmap.
    By default the heatmap is in BGR format.
    :param img: The base image in RGB or BGR format.
    :param mask: The cam mask.
    :param use_rgb: Whether to use an RGB or BGR heatmap, this should be set to True if 'img' is in RGB format.
    :param colormap: The OpenCV colormap to be used.
    :returns: The default image with the cam overlay.
    """
    heatmap = cv2.applyColorMap(np.uint8(255 * mask), colormap)
    if use_rgb:
        heatmap = cv2.cvtColor(heatmap, cv2.COLOR_BGR2RGB)
    heatmap = np.float32(heatmap) / 255

    if np.max(img) > 1:
        raise Exception(
            "The input image should np.float32 in the range [0, 1]")

    cam = 0.7*heatmap + 0.3*img
    # cam = cam / np.max(cam)
    return np.uint8(255 * cam)




def recognize_image(image):
    img_t = eval_transforms(image) 
    # compute output
    output = model(img_t.unsqueeze(0))
    prediction = output.softmax(-1).flatten()
    _,top5_idx = torch.topk(prediction, 5)
    return {id2name[str(i)][0]: float(prediction[i]) for i in top5_idx.tolist()}

eval_transforms = build_transforms(224)


image = gr.inputs.Image()
label = gr.outputs.Label(num_top_classes=5)

gr.Interface(
    description="Bamboo for Image Recognition Demo (https://github.com/Davidzhangyuanhan/Bamboo). Bamboo knows what this object is and what you are doing in a very fine-grain granularity: fratercula arctica (fig.5) and dribbler (fig.2)).",
    fn=recognize_image,
    inputs=["image"],
    outputs=[                   
        label,
    ],
    examples=[
    ["./examples/playing_mahjong.jpg"], 
    ["./examples/dribbler.jpg"], 
    ["./examples/Ferrari-F355.jpg"], 
    ["./examples/northern_oriole.jpg"], 
    ["./examples/fratercula_arctica.jpg"], 
    ["./examples/husky.jpg"], 
    ["./examples/taraxacum_erythrospermum.jpg"], 
    ],
).launch()

gr.markdown('<img id="visitor-badge" alt="visitor badge" src="https://visitor-badge.glitch.me/badge?page_id=cvpr.bamboo_vit-b16_demo" />')