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import numpy as np
from PIL import Image
from tqdm import tqdm
def compute_errors(target, prediction):
thresh = np.maximum((target / prediction), (prediction / target))
a1 = (thresh < 1.25).mean()
a2 = (thresh < 1.25 ** 2).mean()
a3 = (thresh < 1.25 ** 3).mean()
abs_rel = np.mean(np.abs(target - prediction) / target)
sq_rel = np.mean(((target - prediction) ** 2) / target)
rmse = (target - prediction) ** 2
rmse = np.sqrt(rmse.mean())
rmse_log = (np.log(target) - np.log(prediction)) ** 2
rmse_log = np.sqrt(rmse_log.mean())
err = np.log(prediction) - np.log(target)
silog = np.sqrt(np.mean(err ** 2) - np.mean(err) ** 2) * 100
log_10 = (np.abs(np.log10(target) - np.log10(prediction))).mean()
return a1, a2, a3, abs_rel, sq_rel, rmse, rmse_log, silog, log_10
def compute_eval_metrics(test_files):
min_depth_eval = 1e-3
max_depth_eval = 10
num_samples = len(test_files)
a1 = np.zeros(num_samples, np.float32)
a2 = np.zeros(num_samples, np.float32)
a3 = np.zeros(num_samples, np.float32)
abs_rel = np.zeros(num_samples, np.float32)
sq_rel = np.zeros(num_samples, np.float32)
rmse = np.zeros(num_samples, np.float32)
rmse_log = np.zeros(num_samples, np.float32)
silog = np.zeros(num_samples, np.float32)
log10 = np.zeros(num_samples, np.float32)
for i in tqdm(range(num_samples), desc="Calculating metrics for test data", total=num_samples):
sample_path = test_files[i]
target_path = str(sample_path.parent/(sample_path.stem + "_depth.png"))
pred_path = "src/eval/" + str(sample_path.stem) + "_pred.png"
target_image = Image.open(target_path)
pred_image = Image.open(pred_path)
target = np.asarray(target_image)
pred = np.asarray(pred_image)
target = target / 25.0
pred = pred / 25.0
pred[pred < min_depth_eval] = min_depth_eval
pred[pred > max_depth_eval] = max_depth_eval
pred[np.isinf(pred)] = max_depth_eval
target[np.isinf(target)] = 0
target[np.isnan(target)] = 0
valid_mask = np.logical_and(target > min_depth_eval, target < max_depth_eval)
a1[i], a2[i], a3[i], abs_rel[i], sq_rel[i], rmse[i], rmse_log[i], silog[i], log10[i] = \
compute_errors(target[valid_mask], pred[valid_mask])
print("{:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}, {:>7}".format(
'd1', 'd2', 'd3', 'AbsRel', 'SqRel', 'RMSE', 'RMSElog', 'SILog', 'log10'))
print("{:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}, {:7.3f}".format(
a1.mean(), a2.mean(), a3.mean(),
abs_rel.mean(), sq_rel.mean(), rmse.mean(), rmse_log.mean(), silog.mean(), log10.mean()))
return dict(a1=a1.mean(), a2=a2.mean(), a3=a3.mean(),
abs_rel=abs_rel.mean(), sq_rel=sq_rel.mean(),
rmse=rmse.mean(), rmse_log=rmse_log.mean(),
log10=log10.mean(), silog=silog.mean())
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