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release evaluation code & doc

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pq-yang
2026-03-09 08:10:47 +00:00
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evaluation/eval_lr.py
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# This file is modified based on `evaluate_lr.py` from [RobustVideoMatting](https://github.com/PeterL1n/RobustVideoMatting)
# Changes:
# - Adapted for YouTubeMatte/VideoMatte dataset
# - Supported metrics: pha_mad, pha_mse, pha_grad, pha_dtssd, pha_conn
"""
LR (Low-Resolution) evaluation.
Note, the script only does evaluation. You will need to first inference yourself and save the results to disk
Expected directory format for both prediction and ground-truth is (same for VideoMatte):
youtubematte_512x288
├── youtubematte_motion
├── 0000
├── pha
├── 0000.png
├── youtubematte_static
├── 0000
├── pha
├── 0000.png
Prediction must have the exact file structure and file name as the ground-truth,
meaning that if the ground-truth is png/jpg, prediction should be png/jpg.
Example usage:
[YouTubeMatte]
python evaluation/eval_lr.py \
--pred-dir ./data/results/youtubematte_512x288 \
--true-dir ./data/YouTubeMatte/youtubematte_512x288
[VideoMatte]
python evaluation/eval_lr.py \
--pred-dir ./data/results/videomatte_512x288 \
--true-dir ./data/VideoMatte/videomatte_512x288
An excel sheet with evaluation results will be written to "PATH_TO_PREDICTIONS/xxxmatte_512x288.xlsx"
"""
import argparse
import os
import cv2
import numpy as np
import xlsxwriter
from concurrent.futures import ThreadPoolExecutor
from tqdm import tqdm
class Evaluator:
def __init__(self):
self.parse_args()
self.init_metrics()
self.evaluate()
self.write_excel()
def parse_args(self):
parser = argparse.ArgumentParser()
parser.add_argument('--pred-dir', type=str, required=True)
parser.add_argument('--true-dir', type=str, required=True)
parser.add_argument('--num-workers', type=int, default=48)
parser.add_argument('--metrics', type=str, nargs='+', default=[
'pha_mad', 'pha_mse', 'pha_grad', 'pha_dtssd', 'pha_conn'])
self.args = parser.parse_args()
def init_metrics(self):
self.mad = MetricMAD()
self.mse = MetricMSE()
self.grad = MetricGRAD()
self.conn = MetricCONN()
self.dtssd = MetricDTSSD()
def evaluate(self):
tasks = []
position = 0
with ThreadPoolExecutor(max_workers=self.args.num_workers) as executor:
for dataset in sorted(os.listdir(self.args.pred_dir)):
if os.path.isdir(os.path.join(self.args.pred_dir, dataset)):
for clip in sorted(os.listdir(os.path.join(self.args.pred_dir, dataset))):
future = executor.submit(self.evaluate_worker, dataset, clip, position)
tasks.append((dataset, clip, future))
position += 1
self.results = [(dataset, clip, future.result()) for dataset, clip, future in tasks]
def write_excel(self):
workbook = xlsxwriter.Workbook(os.path.join(self.args.pred_dir, f'{os.path.basename(self.args.pred_dir)}.xlsx'))
summarysheet = workbook.add_worksheet('summary')
metricsheets = [workbook.add_worksheet(metric) for metric in self.results[0][-1].keys()]
for i, metric in enumerate(self.results[0][-1].keys()):
summarysheet.write(i, 0, metric)
summarysheet.write(i, 1, f'={metric}!B2')
for row, (dataset, clip, metrics) in enumerate(self.results):
for metricsheet, metric in zip(metricsheets, metrics.values()):
# Write the header
if row == 0:
metricsheet.write(1, 0, 'Average')
metricsheet.write(1, 1, f'=AVERAGE(C2:ZZ2)')
for col in range(len(metric)):
metricsheet.write(0, col + 2, col)
colname = xlsxwriter.utility.xl_col_to_name(col + 2)
metricsheet.write(1, col + 2, f'=AVERAGE({colname}3:{colname}9999)')
metricsheet.write(row + 2, 0, dataset)
metricsheet.write(row + 2, 1, clip)
metricsheet.write_row(row + 2, 2, metric)
workbook.close()
def evaluate_worker(self, dataset, clip, position):
framenames = sorted(os.listdir(os.path.join(self.args.pred_dir, dataset, clip, 'pha')))
metrics = {metric_name : [] for metric_name in self.args.metrics}
pred_pha_tm1 = None
true_pha_tm1 = None
for i, framename in enumerate(tqdm(framenames, desc=f'{dataset} {clip}', position=position, dynamic_ncols=True)):
true_pha = cv2.imread(os.path.join(self.args.true_dir, dataset, clip, 'pha', framename[:-4]+".png"), cv2.IMREAD_GRAYSCALE).astype(np.float32) / 255
pred_pha = cv2.imread(os.path.join(self.args.pred_dir, dataset, clip, 'pha', framename), cv2.IMREAD_GRAYSCALE).astype(np.float32) / 255
if 'pha_mad' in self.args.metrics:
metrics['pha_mad'].append(self.mad(pred_pha, true_pha))
if 'pha_mse' in self.args.metrics:
metrics['pha_mse'].append(self.mse(pred_pha, true_pha))
if 'pha_grad' in self.args.metrics:
metrics['pha_grad'].append(self.grad(pred_pha, true_pha))
if 'pha_conn' in self.args.metrics:
metrics['pha_conn'].append(self.conn(pred_pha, true_pha))
if 'pha_dtssd' in self.args.metrics:
if i == 0:
metrics['pha_dtssd'].append(0)
else:
metrics['pha_dtssd'].append(self.dtssd(pred_pha, pred_pha_tm1, true_pha, true_pha_tm1))
pred_pha_tm1 = pred_pha
true_pha_tm1 = true_pha
return metrics
class MetricMAD:
def __call__(self, pred, true):
return np.abs(pred - true).mean() * 1e3
class MetricMSE:
def __call__(self, pred, true):
return ((pred - true) ** 2).mean() * 1e3
class MetricGRAD:
def __init__(self, sigma=1.4):
self.filter_x, self.filter_y = self.gauss_filter(sigma)
def __call__(self, pred, true):
pred_normed = np.zeros_like(pred)
true_normed = np.zeros_like(true)
cv2.normalize(pred, pred_normed, 1., 0., cv2.NORM_MINMAX)
cv2.normalize(true, true_normed, 1., 0., cv2.NORM_MINMAX)
true_grad = self.gauss_gradient(true_normed).astype(np.float32)
pred_grad = self.gauss_gradient(pred_normed).astype(np.float32)
grad_loss = ((true_grad - pred_grad) ** 2).sum()
return grad_loss / 1000
def gauss_gradient(self, img):
img_filtered_x = cv2.filter2D(img, -1, self.filter_x, borderType=cv2.BORDER_REPLICATE)
img_filtered_y = cv2.filter2D(img, -1, self.filter_y, borderType=cv2.BORDER_REPLICATE)
return np.sqrt(img_filtered_x**2 + img_filtered_y**2)
@staticmethod
def gauss_filter(sigma, epsilon=1e-2):
half_size = np.ceil(sigma * np.sqrt(-2 * np.log(np.sqrt(2 * np.pi) * sigma * epsilon)))
size = int(2 * half_size + 1)
# create filter in x axis
filter_x = np.zeros((size, size))
for i in range(size):
for j in range(size):
filter_x[i, j] = MetricGRAD.gaussian(i - half_size, sigma) * MetricGRAD.dgaussian(
j - half_size, sigma)
# normalize filter
norm = np.sqrt((filter_x**2).sum())
filter_x = filter_x / norm
filter_y = np.transpose(filter_x)
return filter_x, filter_y
@staticmethod
def gaussian(x, sigma):
return np.exp(-x**2 / (2 * sigma**2)) / (sigma * np.sqrt(2 * np.pi))
@staticmethod
def dgaussian(x, sigma):
return -x * MetricGRAD.gaussian(x, sigma) / sigma**2
class MetricCONN:
def __call__(self, pred, true):
step=0.1
thresh_steps = np.arange(0, 1 + step, step)
round_down_map = -np.ones_like(true)
for i in range(1, len(thresh_steps)):
true_thresh = true >= thresh_steps[i]
pred_thresh = pred >= thresh_steps[i]
intersection = (true_thresh & pred_thresh).astype(np.uint8)
# connected components
_, output, stats, _ = cv2.connectedComponentsWithStats(
intersection, connectivity=4)
# start from 1 in dim 0 to exclude background
size = stats[1:, -1]
# largest connected component of the intersection
omega = np.zeros_like(true)
if len(size) != 0:
max_id = np.argmax(size)
# plus one to include background
omega[output == max_id + 1] = 1
mask = (round_down_map == -1) & (omega == 0)
round_down_map[mask] = thresh_steps[i - 1]
round_down_map[round_down_map == -1] = 1
true_diff = true - round_down_map
pred_diff = pred - round_down_map
# only calculate difference larger than or equal to 0.15
true_phi = 1 - true_diff * (true_diff >= 0.15)
pred_phi = 1 - pred_diff * (pred_diff >= 0.15)
connectivity_error = np.sum(np.abs(true_phi - pred_phi))
return connectivity_error / 1000
class MetricDTSSD:
def __call__(self, pred_t, pred_tm1, true_t, true_tm1):
dtSSD = ((pred_t - pred_tm1) - (true_t - true_tm1)) ** 2
dtSSD = np.sum(dtSSD) / true_t.size
dtSSD = np.sqrt(dtSSD)
return dtSSD * 1e2
if __name__ == '__main__':
Evaluator()