vis.py

import sys
import argparse
import cv2
from lib.preprocess import h36m_coco_format, revise_kpts
from lib.hrnet.gen_kpts import gen_video_kpts as hrnet_pose
import os 
import numpy as np
import torch
import torch.nn as nn
import glob
from tqdm import tqdm
import copy

sys.path.append(os.getcwd())
from common.model_poseformer import PoseTransformerV2 as Model
from common.camera import *

import matplotlib
import matplotlib.pyplot as plt 
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.gridspec as gridspec

plt.switch_backend('agg')
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42

def show2Dpose(kps, img):
    connections = [[0, 1], [1, 2], [2, 3], [0, 4], [4, 5],
                   [5, 6], [0, 7], [7, 8], [8, 9], [9, 10],
                   [8, 11], [11, 12], [12, 13], [8, 14], [14, 15], [15, 16]]

    LR = np.array([0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0], dtype=bool)

    lcolor = (255, 0, 0)
    rcolor = (0, 0, 255)
    thickness = 3

    for j,c in enumerate(connections):
        start = map(int, kps[c[0]])
        end = map(int, kps[c[1]])
        start = list(start)
        end = list(end)
        cv2.line(img, (start[0], start[1]), (end[0], end[1]), lcolor if LR[j] else rcolor, thickness)
        cv2.circle(img, (start[0], start[1]), thickness=-1, color=(0, 255, 0), radius=3)
        cv2.circle(img, (end[0], end[1]), thickness=-1, color=(0, 255, 0), radius=3)

    return img


def show3Dpose(vals, ax):
    ax.view_init(elev=15., azim=70)

    lcolor=(0,0,1)
    rcolor=(1,0,0)

    I = np.array( [0, 0, 1, 4, 2, 5, 0, 7,  8,  8, 14, 15, 11, 12, 8,  9])
    J = np.array( [1, 4, 2, 5, 3, 6, 7, 8, 14, 11, 15, 16, 12, 13, 9, 10])

    LR = np.array([0, 1, 0, 1, 0, 1, 0, 0, 0,   1,  0,  0,  1,  1, 0, 0], dtype=bool)

    for i in np.arange( len(I) ):
        x, y, z = [np.array( [vals[I[i], j], vals[J[i], j]] ) for j in range(3)]
        ax.plot(x, y, z, lw=2, color = lcolor if LR[i] else rcolor)

    RADIUS = 0.72
    RADIUS_Z = 0.7

    xroot, yroot, zroot = vals[0,0], vals[0,1], vals[0,2]
    ax.set_xlim3d([-RADIUS+xroot, RADIUS+xroot])
    ax.set_ylim3d([-RADIUS+yroot, RADIUS+yroot])
    ax.set_zlim3d([-RADIUS_Z+zroot, RADIUS_Z+zroot])
    ax.set_aspect('auto') # works fine in matplotlib==2.2.2

    white = (1.0, 1.0, 1.0, 0.0)
    ax.xaxis.set_pane_color(white) 
    ax.yaxis.set_pane_color(white)
    ax.zaxis.set_pane_color(white)

    ax.tick_params('x', labelbottom = False)
    ax.tick_params('y', labelleft = False)
    ax.tick_params('z', labelleft = False)


def get_pose2D(video_path, output_dir):
    cap = cv2.VideoCapture(video_path)
    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)

    print('\nGenerating 2D pose...')
    keypoints, scores = hrnet_pose(video_path, det_dim=416, num_peroson=1, gen_output=True)
    keypoints, scores, valid_frames = h36m_coco_format(keypoints, scores)
    re_kpts = revise_kpts(keypoints, scores, valid_frames)
    print('Generating 2D pose successful!')

    output_dir += 'input_2D/'
    os.makedirs(output_dir, exist_ok=True)

    output_npz = output_dir + 'keypoints.npz'
    np.savez_compressed(output_npz, reconstruction=keypoints)


def img2video(video_path, output_dir):
    cap = cv2.VideoCapture(video_path)
    fps = int(cap.get(cv2.CAP_PROP_FPS)) + 5

    fourcc = cv2.VideoWriter_fourcc(*"mp4v")

    names = sorted(glob.glob(os.path.join(output_dir + 'pose/', '*.png')))
    img = cv2.imread(names[0])
    size = (img.shape[1], img.shape[0])

    videoWrite = cv2.VideoWriter(output_dir + video_name + '.mp4', fourcc, fps, size) 

    for name in names:
        img = cv2.imread(name)
        videoWrite.write(img)

    videoWrite.release()


def showimage(ax, img):
    ax.set_xticks([])
    ax.set_yticks([]) 
    plt.axis('off')
    ax.imshow(img)


def get_pose3D(video_path, output_dir):
    args, _ = argparse.ArgumentParser().parse_known_args()
    args.embed_dim_ratio, args.depth, args.frames = 32, 4, 243
    args.number_of_kept_frames, args.number_of_kept_coeffs = 27, 27
    args.pad = (args.frames - 1) // 2
    args.previous_dir = 'checkpoint/'
    args.n_joints, args.out_joints = 17, 17

    ## Reload 
    cuda_available = torch.cuda.is_available()
    print(f"CUDA available in get_pose3D: {cuda_available}")
    if cuda_available:
        print(f"CUDA device count: {torch.cuda.device_count()}")
        print(f"CUDA device name: {torch.cuda.get_device_name(0)}")
    
    device = torch.device('cuda' if cuda_available else 'cpu')
    print(f"Using device: {device}")
    
    base_model = Model(args=args)
    
    # Always use DataParallel when CUDA is available (checkpoint expects it)
    if cuda_available:
        model = nn.DataParallel(base_model).to(device)
    else:
        model = base_model.to(device)

    model_dict = model.state_dict()
    # Put the pretrained model of PoseFormerV2 in 'checkpoint/']
    # model_path = sorted(glob.glob(os.path.join(args.previous_dir, '27_243_45.2.bin')))
    # Support both local structure and HF Spaces structure
    if os.path.exists("./demo/lib/checkpoint/27_243_45.2.bin"):
        model_path = "./demo/lib/checkpoint/27_243_45.2.bin"
    elif os.path.exists("./lib/checkpoint/27_243_45.2.bin"):
        model_path = "./lib/checkpoint/27_243_45.2.bin"
    else:
        model_path = "./checkpoint/27_243_45.2.bin"

    map_location = device
    pre_dict = torch.load(model_path, map_location=map_location, weights_only=False)
    
    # Handle DataParallel checkpoint mismatch
    state_dict = pre_dict['model_pos']
    from collections import OrderedDict
    new_state_dict = OrderedDict()
    
    # Check if we need to add or remove "module." prefix
    checkpoint_has_module = any(k.startswith('module.') for k in state_dict.keys())
    model_has_module = isinstance(model, nn.DataParallel)
    
    if checkpoint_has_module and not model_has_module:
        # Remove "module." prefix
        for k, v in state_dict.items():
            name = k[7:] if k.startswith('module.') else k
            new_state_dict[name] = v
    elif not checkpoint_has_module and model_has_module:
        # Add "module." prefix
        for k, v in state_dict.items():
            name = 'module.' + k if not k.startswith('module.') else k
            new_state_dict[name] = v
    else:
        # No change needed
        new_state_dict = state_dict
    
    model.load_state_dict(new_state_dict, strict=True)

    model.eval()

    ## input
    keypoints = np.load(output_dir + 'input_2D/keypoints.npz', allow_pickle=True)['reconstruction']

    cap = cv2.VideoCapture(video_path)
    video_length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))

    ## 3D
    print('\nGenerating 3D pose...')
    keypoints_3D = []
    for i in tqdm(range(video_length)):
        ret, img = cap.read()
        if img is None:
            continue
        img_size = img.shape

        ## input frames
        start = max(0, i - args.pad)
        end =  min(i + args.pad, len(keypoints[0])-1)

        input_2D_no = keypoints[0][start:end+1]
        
        left_pad, right_pad = 0, 0
        if input_2D_no.shape[0] != args.frames:
            if i < args.pad:
                left_pad = args.pad - i
            if i > len(keypoints[0]) - args.pad - 1:
                right_pad = i + args.pad - (len(keypoints[0]) - 1)

            input_2D_no = np.pad(input_2D_no, ((left_pad, right_pad), (0, 0), (0, 0)), 'edge')
        
        joints_left =  [4, 5, 6, 11, 12, 13]
        joints_right = [1, 2, 3, 14, 15, 16]

        # input_2D_no += np.random.normal(loc=0.0, scale=5, size=input_2D_no.shape)
        input_2D = normalize_screen_coordinates(input_2D_no, w=img_size[1], h=img_size[0])  

        input_2D_aug = copy.deepcopy(input_2D)
        input_2D_aug[ :, :, 0] *= -1
        input_2D_aug[ :, joints_left + joints_right] = input_2D_aug[ :, joints_right + joints_left]
        input_2D = np.concatenate((np.expand_dims(input_2D, axis=0), np.expand_dims(input_2D_aug, axis=0)), 0)
        # (2, 243, 17, 2)
        
        input_2D = input_2D[np.newaxis, :, :, :, :]

        input_2D = torch.from_numpy(input_2D.astype('float32')).to(device)

        N = input_2D.size(0)

        ## estimation
        output_3D_non_flip = model(input_2D[:, 0]) 
        output_3D_flip     = model(input_2D[:, 1])
        # [1, 1, 17, 3]

        output_3D_flip[:, :, :, 0] *= -1
        output_3D_flip[:, :, joints_left + joints_right, :] = output_3D_flip[:, :, joints_right + joints_left, :] 

        output_3D = (output_3D_non_flip + output_3D_flip) / 2

        output_3D[:, :, 0, :] = 0
        post_out = output_3D[0, 0].cpu().detach().numpy()
        keypoints_3D.append(post_out)
        # print(f'Output 3D shape: {output_3D.shape}, post_out shape: {post_out.shape}, output 3D sample: {output_3D[0]}, post out sample: {post_out}')

        rot =  [0.1407056450843811, -0.1500701755285263, -0.755240797996521, 0.6223280429840088]
        rot = np.array(rot, dtype='float32')
        post_out = camera_to_world(post_out, R=rot, t=0)
        post_out[:, 2] -= np.min(post_out[:, 2])

        input_2D_no = input_2D_no[args.pad]

        ## 2D
        image = show2Dpose(input_2D_no, copy.deepcopy(img))

        output_dir_2D = output_dir +'pose2D/'
        os.makedirs(output_dir_2D, exist_ok=True)
        cv2.imwrite(output_dir_2D + str(('%04d'% i)) + '_2D.png', image)

        ## 3D
        fig = plt.figure(figsize=(9.6, 5.4))
        gs = gridspec.GridSpec(1, 1)
        gs.update(wspace=-0.00, hspace=0.05) 
        ax = plt.subplot(gs[0], projection='3d')
        show3Dpose( post_out, ax)

        output_dir_3D = output_dir +'pose3D/'
        os.makedirs(output_dir_3D, exist_ok=True)
        plt.savefig(output_dir_3D + str(('%04d'% i)) + '_3D.png', dpi=200, format='png', bbox_inches = 'tight')
        plt.clf()
        plt.close(fig)
    
    output_npz = output_dir + 'keypoints_3D.npz'
    np.savez_compressed(output_npz, reconstruction=keypoints_3D)
    print('Generating 3D pose successful!')

    ## all
    image_dir = 'results/' 
    image_2d_dir = sorted(glob.glob(os.path.join(output_dir_2D, '*.png')))
    image_3d_dir = sorted(glob.glob(os.path.join(output_dir_3D, '*.png')))

    print('\nGenerating demo...')
    for i in tqdm(range(len(image_2d_dir))):
        image_2d = plt.imread(image_2d_dir[i])
        image_3d = plt.imread(image_3d_dir[i])

        ## crop
        edge = (image_2d.shape[1] - image_2d.shape[0]) // 2
        image_2d = image_2d[:, edge:image_2d.shape[1] - edge]

        edge = 130
        image_3d = image_3d[edge:image_3d.shape[0] - edge, edge:image_3d.shape[1] - edge]

        ## show
        font_size = 12
        fig = plt.figure(figsize=(15.0, 5.4))
        ax = plt.subplot(121)
        showimage(ax, image_2d)
        ax.set_title("Input", fontsize = font_size)

        ax = plt.subplot(122)
        showimage(ax, image_3d)
        ax.set_title("Reconstruction", fontsize = font_size)

        ## save
        output_dir_pose = output_dir +'pose/'
        os.makedirs(output_dir_pose, exist_ok=True)
        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
        plt.margins(0, 0)
        plt.savefig(output_dir_pose + str(('%04d'% i)) + '_pose.png', dpi=200, bbox_inches = 'tight')
        plt.clf()
        plt.close(fig)

if __name__ == "__main__":
    parser = argparse.ArgumentParser()
    parser.add_argument('--video', type=str, default='sample_video.mp4', help='input video')
    parser.add_argument('--gpu', type=str, default='0', help='GPU device ID (set CUDA_VISIBLE_DEVICES before running if needed)')
    args = parser.parse_args()

    # Note: CUDA_VISIBLE_DEVICES must be set BEFORE importing torch
    # Since torch is imported at the top, setting it here won't work
    # Set it in your environment before running: $env:CUDA_VISIBLE_DEVICES="0" (PowerShell) or export CUDA_VISIBLE_DEVICES=0 (bash)
    
    # Verify CUDA availability
    print(f"CUDA available: {torch.cuda.is_available()}")
    if torch.cuda.is_available():
        print(f"CUDA device count: {torch.cuda.device_count()}")
        print(f"Current device: {torch.cuda.current_device()}")
        print(f"Device name: {torch.cuda.get_device_name(0)}")
        if "CUDA_VISIBLE_DEVICES" in os.environ:
            print(f"CUDA_VISIBLE_DEVICES={os.environ['CUDA_VISIBLE_DEVICES']}")
    else:
        print("WARNING: CUDA is not available!")
        print("This might be because:")
        print("  1. CUDA_VISIBLE_DEVICES was set incorrectly")
        print("  2. PyTorch was installed without CUDA support")
        print("  3. GPU drivers are not installed")
        print("\nTo use GPU, set CUDA_VISIBLE_DEVICES BEFORE running Python:")
        print("  PowerShell: $env:CUDA_VISIBLE_DEVICES='0'")
        print("  Bash: export CUDA_VISIBLE_DEVICES=0")
        print("\nOr don't set it at all to use the default GPU")

    video_path = './demo/video/' + args.video
    video_name = video_path.split('/')[-1].split('.')[0]
    output_dir = './demo/output/' + video_name + '/'

    get_pose2D(video_path, output_dir)
    get_pose3D(video_path, output_dir)
    img2video(video_path, output_dir)
    print('Generating demo successful!')