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216 lines
8.5 KiB
Python
216 lines
8.5 KiB
Python
"""
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HR (High-Resolution) evaluation. We found using numpy is very slow for high resolution, so we moved it to PyTorch using CUDA.
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Note, the script only does evaluation. You will need to first inference yourself and save the results to disk
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Expected directory format for both prediction and ground-truth is:
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videomatte_1920x1080
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├── videomatte_motion
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├── pha
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├── 0000
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├── 0000.png
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├── fgr
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├── 0000
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├── 0000.png
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├── videomatte_static
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├── pha
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├── 0000
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├── 0000.png
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├── fgr
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├── 0000
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├── 0000.png
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Prediction must have the exact file structure and file name as the ground-truth,
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meaning that if the ground-truth is png/jpg, prediction should be png/jpg.
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Example usage:
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python evaluate.py \
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--pred-dir pred/videomatte_1920x1080 \
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--true-dir true/videomatte_1920x1080
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An excel sheet with evaluation results will be written to "pred/videomatte_1920x1080/videomatte_1920x1080.xlsx"
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"""
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import argparse
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import os
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import cv2
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import kornia
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import numpy as np
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import xlsxwriter
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import torch
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from concurrent.futures import ThreadPoolExecutor
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from tqdm import tqdm
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class Evaluator:
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def __init__(self):
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self.parse_args()
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self.init_metrics()
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self.evaluate()
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self.write_excel()
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def parse_args(self):
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parser = argparse.ArgumentParser()
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parser.add_argument('--pred-dir', type=str, required=True)
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parser.add_argument('--true-dir', type=str, required=True)
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parser.add_argument('--num-workers', type=int, default=48)
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parser.add_argument('--metrics', type=str, nargs='+', default=[
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'pha_mad', 'pha_mse', 'pha_grad', 'pha_dtssd', 'fgr_mse'])
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self.args = parser.parse_args()
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def init_metrics(self):
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self.mad = MetricMAD()
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self.mse = MetricMSE()
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self.grad = MetricGRAD()
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self.dtssd = MetricDTSSD()
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def evaluate(self):
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tasks = []
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position = 0
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with ThreadPoolExecutor(max_workers=self.args.num_workers) as executor:
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for dataset in sorted(os.listdir(self.args.pred_dir)):
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if os.path.isdir(os.path.join(self.args.pred_dir, dataset)):
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for clip in sorted(os.listdir(os.path.join(self.args.pred_dir, dataset))):
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future = executor.submit(self.evaluate_worker, dataset, clip, position)
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tasks.append((dataset, clip, future))
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position += 1
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self.results = [(dataset, clip, future.result()) for dataset, clip, future in tasks]
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def write_excel(self):
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workbook = xlsxwriter.Workbook(os.path.join(self.args.pred_dir, f'{os.path.basename(self.args.pred_dir)}.xlsx'))
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summarysheet = workbook.add_worksheet('summary')
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metricsheets = [workbook.add_worksheet(metric) for metric in self.results[0][2].keys()]
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for i, metric in enumerate(self.results[0][2].keys()):
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summarysheet.write(i, 0, metric)
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summarysheet.write(i, 1, f'={metric}!B2')
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for row, (dataset, clip, metrics) in enumerate(self.results):
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for metricsheet, metric in zip(metricsheets, metrics.values()):
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# Write the header
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if row == 0:
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metricsheet.write(1, 0, 'Average')
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metricsheet.write(1, 1, f'=AVERAGE(C2:ZZ2)')
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for col in range(len(metric)):
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metricsheet.write(0, col + 2, col)
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colname = xlsxwriter.utility.xl_col_to_name(col + 2)
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metricsheet.write(1, col + 2, f'=AVERAGE({colname}3:{colname}9999)')
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metricsheet.write(row + 2, 0, dataset)
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metricsheet.write(row + 2, 1, clip)
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metricsheet.write_row(row + 2, 2, metric)
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workbook.close()
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def evaluate_worker(self, dataset, clip, position):
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framenames = sorted(os.listdir(os.path.join(self.args.pred_dir, dataset, clip, 'pha')))
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metrics = {metric_name : [] for metric_name in self.args.metrics}
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pred_pha_tm1 = None
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true_pha_tm1 = None
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for i, framename in enumerate(tqdm(framenames, desc=f'{dataset} {clip}', position=position, dynamic_ncols=True)):
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true_pha = cv2.imread(os.path.join(self.args.true_dir, dataset, clip, 'pha', framename), cv2.IMREAD_GRAYSCALE)
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pred_pha = cv2.imread(os.path.join(self.args.pred_dir, dataset, clip, 'pha', framename), cv2.IMREAD_GRAYSCALE)
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true_pha = torch.from_numpy(true_pha).cuda(non_blocking=True).float().div_(255)
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pred_pha = torch.from_numpy(pred_pha).cuda(non_blocking=True).float().div_(255)
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if 'pha_mad' in self.args.metrics:
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metrics['pha_mad'].append(self.mad(pred_pha, true_pha))
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if 'pha_mse' in self.args.metrics:
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metrics['pha_mse'].append(self.mse(pred_pha, true_pha))
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if 'pha_grad' in self.args.metrics:
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metrics['pha_grad'].append(self.grad(pred_pha, true_pha))
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if 'pha_conn' in self.args.metrics:
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metrics['pha_conn'].append(self.conn(pred_pha, true_pha))
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if 'pha_dtssd' in self.args.metrics:
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if i == 0:
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metrics['pha_dtssd'].append(0)
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else:
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metrics['pha_dtssd'].append(self.dtssd(pred_pha, pred_pha_tm1, true_pha, true_pha_tm1))
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pred_pha_tm1 = pred_pha
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true_pha_tm1 = true_pha
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if 'fgr_mse' in self.args.metrics:
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true_fgr = cv2.imread(os.path.join(self.args.true_dir, dataset, clip, 'fgr', framename), cv2.IMREAD_COLOR)
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pred_fgr = cv2.imread(os.path.join(self.args.pred_dir, dataset, clip, 'fgr', framename), cv2.IMREAD_COLOR)
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true_fgr = torch.from_numpy(true_fgr).float().div_(255)
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pred_fgr = torch.from_numpy(pred_fgr).float().div_(255)
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true_msk = true_pha > 0
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metrics['fgr_mse'].append(self.mse(pred_fgr[true_msk], true_fgr[true_msk]))
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return metrics
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class MetricMAD:
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def __call__(self, pred, true):
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return (pred - true).abs_().mean() * 1e3
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class MetricMSE:
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def __call__(self, pred, true):
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return ((pred - true) ** 2).mean() * 1e3
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class MetricGRAD:
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def __init__(self, sigma=1.4):
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self.filter_x, self.filter_y = self.gauss_filter(sigma)
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self.filter_x = torch.from_numpy(self.filter_x).unsqueeze(0).cuda()
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self.filter_y = torch.from_numpy(self.filter_y).unsqueeze(0).cuda()
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def __call__(self, pred, true):
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true_grad = self.gauss_gradient(true)
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pred_grad = self.gauss_gradient(pred)
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return ((true_grad - pred_grad) ** 2).sum() / 1000
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def gauss_gradient(self, img):
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img_filtered_x = kornia.filters.filter2D(img[None, None, :, :], self.filter_x, border_type='replicate')[0, 0]
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img_filtered_y = kornia.filters.filter2D(img[None, None, :, :], self.filter_y, border_type='replicate')[0, 0]
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return (img_filtered_x**2 + img_filtered_y**2).sqrt()
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@staticmethod
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def gauss_filter(sigma, epsilon=1e-2):
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half_size = np.ceil(sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon)))
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size = np.int(2 * half_size + 1)
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# create filter in x axis
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filter_x = np.zeros((size, size))
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for i in range(size):
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for j in range(size):
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filter_x[i, j] = MetricGRAD.gaussian(i - half_size, sigma) * MetricGRAD.dgaussian(
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j - half_size, sigma)
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# normalize filter
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norm = np.sqrt((filter_x**2).sum())
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filter_x = filter_x / norm
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filter_y = np.transpose(filter_x)
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return filter_x, filter_y
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@staticmethod
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def gaussian(x, sigma):
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return np.exp(-x**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi))
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@staticmethod
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def dgaussian(x, sigma):
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return -x * MetricGRAD.gaussian(x, sigma) / sigma**2
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class MetricDTSSD:
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def __call__(self, pred_t, pred_tm1, true_t, true_tm1):
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dtSSD = ((pred_t - pred_tm1) - (true_t - true_tm1)) ** 2
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dtSSD = dtSSD.sum() / true_t.numel()
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dtSSD = dtSSD.sqrt()
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return dtSSD * 1e2
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if __name__ == '__main__':
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Evaluator() |