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val_find_all_empty.py
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val_find_all_empty.py
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import os
import cv2
import numpy as np
import pandas as pd
from tqdm import tqdm
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
def dice_coef_numpy(preds, labels):
smooth = 1e-6
intersection = np.sum(np.float32(preds) * np.float32(labels), axis=(2, 3))
union = np.sum(np.float32(preds), axis=(2,3)) + np.sum(np.float32(labels), axis=(2,3))
# class 별로 찍게 하자 [N, C]
dice = np.mean((2. * intersection + smooth) / (union + smooth), axis=0)
return dice
################################
fold_df = pd.read_csv('data/folds/5fold_1.csv', engine='python')
fold_df = fold_df.loc[fold_df['split'] == 'val'].reset_index(drop=True)
img_names = fold_df['Image'].values
answer = np.load('validation/val_5fold_1.npy')
pred = np.load('validation/fold1_ensemble2_0.6633_0.6648.npy')
pred_processed = np.load('validation/fold1_post_processed/base.npy') # [1109,4,350,525]
pred_04 = np.load('validation/fold1_post_processed/0.4.npy') # [1109,4,350,525]
################################
all_empty_count = 0
all_empty_idxs = []
for i in range(pred_04.shape[0]):
if np.sum(pred_04[i]) == 0:
all_empty_count += 1
all_empty_idxs.append(i)
print(all_empty_count)
print(all_empty_idxs)
################################
answer_all_empty = answer[all_empty_idxs]
print(answer_all_empty.shape, answer_all_empty.dtype)
pred_all_empty = pred[all_empty_idxs]
print(pred_all_empty.shape, pred_all_empty.dtype)
################################
'''
values = []
for i in range(4):
class_values = []
for j in tqdm(range(answer_all_empty.shape[0])):
_answer = answer_all_empty[j,i]
_pred = pred_all_empty[j,i]
_pred = cv2.resize(_pred, (525,350), interpolation=cv2.INTER_LINEAR)
_pred = cv2.bitwise_and(_pred,_pred, mask=_answer)
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# x_data, y_data = np.meshgrid(np.arange(_pred.shape[1]),
# np.arange(_pred.shape[0]))
# x_data = x_data.flatten()
# y_data = y_data.flatten()
# z_data = _pred.flatten()
# ax.bar3d(x_data,
# y_data,
# np.zeros(len(z_data)),
# 1, 1, z_data)
# plt.show()
_pred = _pred.reshape(-1)
for k in range(350*525):
if _pred[k] != 0:
class_values.append(_pred[k])
# print(class_values)
plt.hist(class_values, bins=100, range=(0,0.5))
plt.show()
# values.append(class_values)
# print(values)
'''
dice = dice_coef_numpy(pred_processed, answer)
dice_total = np.mean(dice)
print(dice_total)
pred_processed[all_empty_idxs] = pred_04[all_empty_idxs]
dice = dice_coef_numpy(pred_processed, answer)
dice_total = np.mean(dice)
print(dice_total)