EasyMocap/easymocap/mytools/file_utils.py

'''
  @ Date: 2021-03-15 12:23:12
  @ Author: Qing Shuai
  @ LastEditors: Qing Shuai
  @ LastEditTime: 2021-04-01 16:17:34
  @ FilePath: /EasyMocap/easymocap/mytools/file_utils.py
'''
import os
import json
import numpy as np
from os.path import join

mkdir = lambda x:os.makedirs(x, exist_ok=True)
mkout = lambda x:mkdir(os.path.dirname(x))

def read_json(path):
    assert os.path.exists(path), path
    with open(path) as f:
        data = json.load(f)
    return data

def save_json(file, data):
    if not os.path.exists(os.path.dirname(file)):
        os.makedirs(os.path.dirname(file))
    with open(file, 'w') as f:
        json.dump(data, f, indent=4)

def getFileList(root, ext='.jpg'):
    files = []
    dirs = os.listdir(root)
    while len(dirs) > 0:
        path = dirs.pop()
        fullname = join(root, path)
        if os.path.isfile(fullname) and fullname.endswith(ext):
            files.append(path)
        elif os.path.isdir(fullname):
            for s in os.listdir(fullname):
                newDir = join(path, s)
                dirs.append(newDir)
    files = sorted(files)
    return files

def read_annot(annotname, mode='body25'):
    data = read_json(annotname)
    if not isinstance(data, list):
        data = data['annots']
    for i in range(len(data)):
        if 'id' not in data[i].keys():
            data[i]['id'] = data[i].pop('personID')
        if 'keypoints2d' in data[i].keys() and 'keypoints' not in data[i].keys():
            data[i]['keypoints'] = data[i].pop('keypoints2d')
        for key in ['bbox', 'keypoints', 'handl2d', 'handr2d', 'face2d']:
            if key not in data[i].keys():continue
            data[i][key] = np.array(data[i][key])
            if key == 'face2d':
                # TODO: Make parameters, 17 is the offset for the eye brows,
                # etc. 51 is the total number of FLAME compatible landmarks
                data[i][key] = data[i][key][17:17+51, :]
        data[i]['bbox'] = data[i]['bbox'][:5]
        if data[i]['bbox'][-1] < 0.001:
            # print('{}/{} bbox conf = 0, may be error'.format(annotname, i))
            data[i]['bbox'][-1] = 1
        if mode == 'body25':
            data[i]['keypoints'] = data[i]['keypoints']
        elif mode == 'body15':
            data[i]['keypoints'] = data[i]['keypoints'][:15, :]
        elif mode == 'total':
            data[i]['keypoints'] = np.vstack([data[i][key] for key in ['keypoints', 'handl2d', 'handr2d', 'face2d']])
        elif mode == 'bodyhand':
            data[i]['keypoints'] = np.vstack([data[i][key] for key in ['keypoints', 'handl2d', 'handr2d']])
        elif mode == 'bodyhandface':
            data[i]['keypoints'] = np.vstack([data[i][key] for key in ['keypoints', 'handl2d', 'handr2d', 'face2d']])
        conf = data[i]['keypoints'][..., -1]
        conf[conf<0] = 0
    data.sort(key=lambda x:x['id'])
    return data

def write_common_results(dumpname, results, keys, fmt='%.3f'):
    mkout(dumpname)
    format_out = {'float_kind':lambda x: fmt % x}
    with open(dumpname, 'w') as f:
        f.write('[\n')
        for idata, data in enumerate(results):
            f.write('    {\n')
            output = {}
            output['id'] = data['id']
            for key in keys:
                if key not in data.keys():continue
                output[key] = np.array2string(data[key], max_line_width=1000, separator=', ', formatter=format_out)
            for key in output.keys():
                f.write('        \"{}\": {}'.format(key, output[key]))
                if key != keys[-1]:
                    f.write(',\n')
                else:
                    f.write('\n')
            f.write('    }')
            if idata != len(results) - 1:
                f.write(',\n')
            else:
                f.write('\n')
        f.write(']\n')

def write_keypoints3d(dumpname, results):
    # TODO:rewrite it
    keys = ['keypoints3d']
    write_common_results(dumpname, results, keys, fmt='%.3f')

def write_smpl(dumpname, results):
    keys = ['Rh', 'Th', 'poses', 'expression', 'shapes']
    write_common_results(dumpname, results, keys)


def get_bbox_from_pose(pose_2d, img, rate = 0.1):
    # this function returns bounding box from the 2D pose
    # here use pose_2d[:, -1] instead of pose_2d[:, 2]
    # because when vis reprojection, the result will be (x, y, depth, conf)
    validIdx = pose_2d[:, -1] > 0
    if validIdx.sum() == 0:
        return [0, 0, 100, 100, 0]
    y_min = int(min(pose_2d[validIdx, 1]))
    y_max = int(max(pose_2d[validIdx, 1]))
    x_min = int(min(pose_2d[validIdx, 0]))
    x_max = int(max(pose_2d[validIdx, 0]))
    dx = (x_max - x_min)*rate
    dy = (y_max - y_min)*rate
    # 后面加上类别这些
    bbox = [x_min-dx, y_min-dy, x_max+dx, y_max+dy, 1]
    correct_bbox(img, bbox)
    return bbox

def correct_bbox(img, bbox):
    # this function corrects the bbox, which is out of image
    w = img.shape[0]
    h = img.shape[1]
    if bbox[2] <= 0 or bbox[0] >= h or bbox[1] >= w or bbox[3] <= 0:
        bbox[4] = 0
    return bbox

def merge_params(param_list, share_shape=True):
    output = {}
    for key in ['poses', 'shapes', 'Rh', 'Th', 'expression']:
        if key in param_list[0].keys():
            output[key] = np.vstack([v[key] for v in param_list])
    if share_shape:
        output['shapes'] = output['shapes'].mean(axis=0, keepdims=True)
    return output

def select_nf(params_all, nf):
    output = {}
    for key in ['poses', 'Rh', 'Th']:
        output[key] = params_all[key][nf:nf+1, :]
    if 'expression' in params_all.keys():
        output['expression'] = params_all['expression'][nf:nf+1, :]
    if params_all['shapes'].shape[0] == 1:
        output['shapes'] = params_all['shapes']
    else:
        output['shapes'] = params_all['shapes'][nf:nf+1, :]
    return output