Detectron2/ensemble.py at main · Kora-Scenes/Detectron2 · GitHub

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import warnings as wr
import glob
import os
import cv2
import numpy as np

wr.filterwarnings("ignore")

with open('/home/yln1kor/nikhil-test/mrcnn_masks.npy','rb') as f:
    mrcnn_masks = np.load(f)

with open('/home/yln1kor/nikhil-test/ptrend_masks.npy','rb') as f:
    ptrend_masks = np.load(f)


# print(mrcnn_masks, mrcnn_masks.shape)
# print(ptrend_masks,ptrend_masks.shape)

# models = []
# models.append(mrcnn_masks)
# models.append(ptrend_masks)
# preds = [model for model in models]
# preds = np.array(preds)
# summed = np.sum(preds, axis = 0)

# print(mrcnn_masks.shape)
# ensemble_prediction = np.argmax(summed, axis = 1)
# print(ensemble_prediction)
# print(ensemble_prediction.shape)
# cv2.imshow('ensemble',ensemble_prediction.astype(np.uint8)*255)
# cv2.waitKey(0)

# prediction1 = model1.predict_classes(X_test)
# prediction2 = model2.predict_classes(X_test)

# accuracy1 = accuracy_score(y_test, prediction1)
# accuracy2 = accuracy_score(y_test, prediction2)
# ensemble_accuracy = accuracy_score(y_test, ensemble_prediction)


path = '/home/yln1kor/Downloads/kitti_official_semantic/training'
path_images = path + '/image_2'
path_instance = path + '/instance'
path_semantic = path + '/semantic_rgb'

gt_masks = []
c = 1
for imageName in sorted(glob.glob(os.path.join(path_semantic, '*.png'))):
    im = cv2.imread(imageName)
    mask = (im == [142,0,0]).all(-1)
    gt_masks.append(mask)
    c += 1
    if c == 10:
        break

im_predmasks = []
for i in range(len(gt_masks)):
    pred_mask = np.full(gt_masks[0].shape,False, dtype =bool)
    pred_mask = np.logical_or(pred_mask,ptrend_masks[i])
    pred_mask = np.logical_and(pred_mask,mrcnn_masks[i])
    im_predmasks.append(pred_mask)
    cv2.imshow('ensemble_masks',pred_mask.astype(np.uint8)*255)
    cv2.waitKey(0)

sum_IOU = 0
sum_DSC = 0
for i in range(len(gt_masks)):
    gt = gt_masks[i]
    pred = im_predmasks[i]
    intersection = np.logical_and(gt,pred)
    union = np.logical_or(gt,pred)
    IOU = np.sum(intersection) / np.sum(union)
    sum_IOU += IOU
    Dice_coeff = 2 * np.sum(intersection) / (np.sum(gt) + np.sum(pred))
    sum_DSC += Dice_coeff
    # print('IOU:',IOU, 'Dice Coeff:',Dice_coeff)

print('IOU',sum_IOU/len(gt_masks))
print('DSC',sum_DSC/len(gt_masks))

def ensemble():
    pass