app.py

import argparse
# Copyright (c) OpenMMLab. All rights reserved.
import os
import random


os.system('python setup.py develop')

import gradio as gr
import numpy as np
import torch
from PIL import ImageDraw, Image
from matplotlib import pyplot as plt
from mmcv import Config
from mmcv.runner import load_checkpoint
from mmpose.core import wrap_fp16_model
from mmpose.models import build_posenet
from torchvision import transforms
from demo import Resize_Pad
from models import *
import matplotlib

matplotlib.use('agg')


def plot_results(support_img, query_img, support_kp, support_w, query_kp,
                 query_w, skeleton,
                 initial_proposals, prediction, radius=6):
    h, w, c = support_img.shape
    prediction = prediction[-1].cpu().numpy() * h
    query_img = (query_img - np.min(query_img)) / (
            np.max(query_img) - np.min(query_img))
    for id, (img, w, keypoint) in enumerate(zip([query_img],
                                                [query_w],
                                                [prediction])):
        f, axes = plt.subplots()
        plt.imshow(img)
        for k in range(keypoint.shape[0]):
            if w[k] > 0:
                kp = keypoint[k, :2]
                c = (1, 0, 0, 0.75) if w[k] == 1 else (0, 0, 1, 0.6)
                patch = plt.Circle(kp, radius, color=c)
                axes.add_patch(patch)
                axes.text(kp[0], kp[1], k)
                plt.draw()
        for l, limb in enumerate(skeleton):
            kp = keypoint[:, :2]
            if l > len(COLORS) - 1:
                c = [x / 255 for x in random.sample(range(0, 255), 3)]
            else:
                c = [x / 255 for x in COLORS[l]]
            if w[limb[0]] > 0 and w[limb[1]] > 0:
                patch = plt.Line2D([kp[limb[0], 0], kp[limb[1], 0]],
                                   [kp[limb[0], 1], kp[limb[1], 1]],
                                   linewidth=6, color=c, alpha=0.6)
                axes.add_artist(patch)
        plt.axis('off')  # command for hiding the axis.
        plt.subplots_adjust(0, 0, 1, 1, 0, 0)
        return plt


COLORS = [
    [255, 85, 0], [255, 170, 0], [255, 255, 0], [170, 255, 0],
    [85, 255, 0], [0, 255, 0], [0, 255, 85], [0, 255, 170], [0, 255, 255],
    [0, 170, 255], [0, 85, 255], [0, 0, 255], [85, 0, 255], [170, 0, 255],
    [255, 0, 255], [255, 0, 170], [255, 0, 85], [255, 0, 0]
]

def process(query_img, state,
            cfg_path='configs/demo_b.py'):
    cfg = Config.fromfile(cfg_path)
    width, height, _ = state['original_support_image'].shape
    kp_src_np = np.array(state['kp_src']).copy().astype(np.float32)
    kp_src_np[:, 0] = kp_src_np[:,0] / (width // 2) * cfg.model.encoder_config.img_size
    kp_src_np[:, 1] = kp_src_np[:,1] / (height // 2) * cfg.model.encoder_config.img_size
    kp_src_np = np.flip(kp_src_np, 1).copy()
    kp_src_tensor = torch.tensor(kp_src_np).float()
    preprocess = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
        Resize_Pad(cfg.model.encoder_config.img_size,
                   cfg.model.encoder_config.img_size)])

    if len(state['skeleton']) == 0:
        state['skeleton'] = [(0, 0)]

    support_img = preprocess(state['original_support_image']).flip(0)[None]
    np_query = np.array(query_img)[:, :, ::-1].copy()
    q_img = preprocess(np_query).flip(0)[None]
    # Create heatmap from keypoints
    genHeatMap = TopDownGenerateTargetFewShot()
    data_cfg = cfg.data_cfg
    data_cfg['image_size'] = np.array([cfg.model.encoder_config.img_size,
                                       cfg.model.encoder_config.img_size])
    data_cfg['joint_weights'] = None
    data_cfg['use_different_joint_weights'] = False
    kp_src_3d = torch.cat(
        (kp_src_tensor, torch.zeros(kp_src_tensor.shape[0], 1)), dim=-1)
    kp_src_3d_weight = torch.cat(
        (torch.ones_like(kp_src_tensor),
         torch.zeros(kp_src_tensor.shape[0], 1)), dim=-1)
    target_s, target_weight_s = genHeatMap._msra_generate_target(data_cfg,
                                                                 kp_src_3d,
                                                                 kp_src_3d_weight,
                                                                 sigma=1)
    target_s = torch.tensor(target_s).float()[None]
    target_weight_s = torch.ones_like(
        torch.tensor(target_weight_s).float()[None])

    data = {
        'img_s': [support_img],
        'img_q': q_img,
        'target_s': [target_s],
        'target_weight_s': [target_weight_s],
        'target_q': None,
        'target_weight_q': None,
        'return_loss': False,
        'img_metas': [{'sample_skeleton': [state['skeleton']],
                       'query_skeleton': state['skeleton'],
                       'sample_joints_3d': [kp_src_3d],
                       'query_joints_3d': kp_src_3d,
                       'sample_center': [kp_src_tensor.mean(dim=0)],
                       'query_center': kp_src_tensor.mean(dim=0),
                       'sample_scale': [
                           kp_src_tensor.max(dim=0)[0] -
                           kp_src_tensor.min(dim=0)[0]],
                       'query_scale': kp_src_tensor.max(dim=0)[0] -
                                      kp_src_tensor.min(dim=0)[0],
                       'sample_rotation': [0],
                       'query_rotation': 0,
                       'sample_bbox_score': [1],
                       'query_bbox_score': 1,
                       'query_image_file': '',
                       'sample_image_file': [''],
                       }]
    }
    # Load model
    model = build_posenet(cfg.model)
    fp16_cfg = cfg.get('fp16', None)
    if fp16_cfg is not None:
        wrap_fp16_model(model)
    load_checkpoint(model, checkpoint_path, map_location='cpu')
    model.eval()
    with torch.no_grad():
        outputs = model(**data)
    # visualize results
    vis_s_weight = target_weight_s[0]
    vis_q_weight = target_weight_s[0]
    vis_s_image = support_img[0].detach().cpu().numpy().transpose(1, 2, 0)
    vis_q_image = q_img[0].detach().cpu().numpy().transpose(1, 2, 0)
    support_kp = kp_src_3d
    out = plot_results(vis_s_image,
                       vis_q_image,
                       support_kp,
                       vis_s_weight,
                       None,
                       vis_q_weight,
                       state['skeleton'],
                       None,
                       torch.tensor(outputs['points']).squeeze(0),
                       )
    return out, state


with gr.Blocks() as demo:
    state = gr.State({
        'kp_src': [],
        'skeleton': [],
        'count': 0,
        'color_idx': 0,
        'prev_pt': None,
        'prev_pt_idx': None,
        'prev_clicked': None,
        'original_support_image': None,
    })

    gr.Markdown('''
    # Pose Anything Demo
    We present a novel approach to category agnostic pose estimation that 
    leverages the inherent geometrical relations between keypoints through a 
    newly designed Graph Transformer Decoder. By capturing and incorporating 
    this crucial structural information, our method enhances the accuracy of 
    keypoint localization, marking a significant departure from conventional 
    CAPE techniques that treat keypoints as isolated entities.
    ### [Paper](https://arxiv.org/abs/2311.17891) | [Official Repo](https://github.com/orhir/PoseAnything) 
    ## Instructions
    1. Upload an image of the object you want to pose on the **left** image.
    2. Click on the **left** image to mark keypoints.
    3. Click on the keypoints on the **right** image to mark limbs.
    4. Upload an image of the object you want to pose to the query image (
    **bottom**).
    5. Click **Evaluate** to pose the query image.
    ''')
    with gr.Row():
        support_img = gr.Image(label="Support Image",
                               type="pil",
                               info='Click to mark keypoints').style(
            height=400, width=400)
        posed_support = gr.Image(label="Posed Support Image",
                                 type="pil",
                                 interactive=False).style(height=400,
                                                          width=400)
    with gr.Row():
        query_img = gr.Image(label="Query Image",
                             type="pil").style(height=400, width=400)
    with gr.Row():
        eval_btn = gr.Button(value="Evaluate")
    with gr.Row():
        output_img = gr.Plot(label="Output Image", height=400, width=400)


    def get_select_coords(kp_support,
                          limb_support,
                          state,
                          evt: gr.SelectData,
                          r=0.015):
        # global original_support_image
        # if len(kp_src) == 0:
        #     original_support_image = np.array(kp_support)[:, :,
        #                              ::-1].copy()
        pixels_in_queue = set()
        pixels_in_queue.add((evt.index[1], evt.index[0]))
        while len(pixels_in_queue) > 0:
            pixel = pixels_in_queue.pop()
            if pixel[0] is not None and pixel[
                1] is not None and pixel not in state['kp_src']:
                state['kp_src'].append(pixel)
            else:
                print("Invalid pixel")
            if limb_support is None:
                canvas_limb = kp_support
            else:
                canvas_limb = limb_support
            canvas_kp = kp_support
            w, h = canvas_kp.size
            draw_pose = ImageDraw.Draw(canvas_kp)
            draw_limb = ImageDraw.Draw(canvas_limb)
            r = int(r * w)
            leftUpPoint = (pixel[1] - r, pixel[0] - r)
            rightDownPoint = (pixel[1] + r, pixel[0] + r)
            twoPointList = [leftUpPoint, rightDownPoint]
            draw_pose.ellipse(twoPointList, fill=(255, 0, 0, 255))
            draw_limb.ellipse(twoPointList, fill=(255, 0, 0, 255))

        return canvas_kp, canvas_limb, state


    def get_limbs(kp_support,
                  state,
                  evt: gr.SelectData,
                  r=0.02, width=0.02):
        curr_pixel = (evt.index[1], evt.index[0])
        pixels_in_queue = set()
        pixels_in_queue.add((evt.index[1], evt.index[0]))
        canvas_kp = kp_support
        w, h = canvas_kp.size
        r = int(r * w)
        width = int(width * w)
        while len(pixels_in_queue) > 0 and curr_pixel != state['prev_clicked']:
            pixel = pixels_in_queue.pop()
            state['prev_clicked'] = pixel
            closest_point = min(state['kp_src'],
                                key=lambda p: (p[0] - pixel[0]) ** 2 +
                                              (p[1] - pixel[1]) ** 2)
            closest_point_index = state['kp_src'].index(closest_point)
            draw_limb = ImageDraw.Draw(canvas_kp)
            if state['color_idx'] < len(COLORS):
                c = COLORS[state['color_idx']]
            else:
                c = random.choices(range(256), k=3)
            leftUpPoint = (closest_point[1] - r, closest_point[0] - r)
            rightDownPoint = (closest_point[1] + r, closest_point[0] + r)
            twoPointList = [leftUpPoint, rightDownPoint]
            draw_limb.ellipse(twoPointList, fill=tuple(c))
            if state['count'] == 0:
                state['prev_pt'] = closest_point[1], closest_point[0]
                state['prev_pt_idx'] = closest_point_index
                state['count'] = state['count'] + 1
            else:
                if state['prev_pt_idx'] != closest_point_index:
                    # Create Line and add Limb
                    draw_limb.line(
                        [state['prev_pt'], (closest_point[1], closest_point[0])],
                        fill=tuple(c),
                        width=width)
                    state['skeleton'].append((state['prev_pt_idx'], closest_point_index))
                    state['color_idx'] = state['color_idx'] + 1
                else:
                    draw_limb.ellipse(twoPointList, fill=(255, 0, 0, 255))
                state['count'] = 0
        return canvas_kp, state


    def set_qery(support_img, state):
        state['skeleton'].clear()
        state['kp_src'].clear()
        state['original_support_image'] = np.array(support_img)[:, :, ::-1].copy()
        width, height = support_img.size
        support_img = support_img.resize((width // 2, width // 2), Image.Resampling.LANCZOS)
        return support_img, support_img, state


    support_img.select(get_select_coords,
                       [support_img, posed_support, state],
                       [support_img, posed_support, state])
    support_img.upload(set_qery,
                       inputs=[support_img, state],
                       outputs=[support_img, posed_support, state])
    posed_support.select(get_limbs,
                         [posed_support, state],
                         [posed_support, state])
    eval_btn.click(fn=process,
                   inputs=[query_img, state],
                   outputs=[output_img, state])


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Pose Anything Demo')
    parser.add_argument('--checkpoint',
                        help='checkpoint path',
                        default='1shot-swin_graph_split1.pth')
    args = parser.parse_args()
    checkpoint_path = args.checkpoint
    print("Loading checkpoint from {}".format(checkpoint_path))
    print(os.path.exists(checkpoint_path))
    demo.launch()