def CalcSAD(input_png_path, GT_png_path):
img_input = imread(input_png_path)
img_GT = imread(GT_png_path)
# reshape for loop
img_input = np.reshape(img_input,(-1,1,1,3))
img_GT = np.reshape(img_GT,(-1,1,1,3))
MODEL = CreateModel()
MODEL.load_weights('weights_lecun_normal_Adamax.hdf5')
img_result = MODEL.predict(img_input, batch_size=256**3)
img_result = im2uint8(img_result)
img_GT = im2uint8(img_GT)
absdiff = np.abs(img_result - img_GT)
SAD = np.sum(absdiff,axis=1)
maxSAD = np.max(SAD)
avgSAD = np.mean(SAD)
return maxSAD, avgSAD
img_GT = imread(png_GT)
img_GT = np.reshape(img_GT, (-1, 1, 1, 3))
img_GT = im2uint8(img_GT)
CSV = f'./experiments/{DIR}/result.csv'
if os.path.isfile(CSV):
DF = pd.read_csv(CSV)
else:
DF = pd.DataFrame(columns=['hdf5', 'maxSAD', 'avgSAD'])
hdf5s = sorted(glob(f'./experiments/{DIR}/HDF5/*.hdf5',
recursive=True))[5000::3]
hdf5s = [i for i in hdf5s if i not in DF['hdf5'].tolist()]
for hdf5 in tqdm(hdf5s):
MODEL.load_weights(hdf5)
img_result = MODEL.predict(img_input, batch_size=256**3)
img_result = im2uint8(img_result)
# tf.keras.preprocessing.image.save_img( 'test.png', np.reshape(img_result,(256*16,256*16,3)))
maxSAD, avgSAD = calcSAD(img_result, img_GT)
DF = DF.append({
'hdf5': hdf5,
'maxSAD': maxSAD,
'avgSAD': avgSAD
},
ignore_index=True)
DF = DF.sort_values(by=['maxSAD', 'avgSAD'])
DF.to_csv(CSV, index=False)
from keras.applications.mobilenetv2 import preprocess_input
import os
from keras.preprocessing.image import load_img, img_to_array
from model import CreateModel
import numpy as np
img = img_to_array(
load_img(os.sys.argv[1], target_size=(128, 128)).convert('RGB'))
img = preprocess_input(img)
batch = np.expand_dims(img, axis=0)
model = CreateModel()
model.load_weights('model.h5')
predictions = model.predict(batch)
print(predictions[0])
