import numpy as np def hist_match ( source , template ): """ Adjust the pixel values of a grayscale image such that its histogram matches that of a target image Arguments: ----------- source: np.ndarray Image to transform; the histogram is computed over the flattened array template: np.ndarray Template image; can have different dimensions to source Returns: ----------- matched: np.ndarray The transformed output image """ oldshape = source . shape source = source . ravel () template = template . ravel () # get the set of unique pixel values and their corresponding indices and # counts s_values , bin_idx , s_counts = np . unique ( source , return_inverse = True , return_counts = True ) t_values , t_counts = np . unique ( template , return_counts = True ) ...
python train_frcnn.py -o simple -p my_data.txt
답글삭제학습시키기
python test_frcnn.py -p /home/scrapmetal/yhenon-keras-frcnn/test
답글삭제테스트 하기.
my_data.txt
답글삭제./metal/1.png,0,0,78,76,metal
./metal/2.png,0,0,78,76,metal
./metal/3.png,0,0,78,76,metal
./metal/4.png,0,0,78,76,metal
프리레퀴짓즈
답글삭제h5py
Keras==2.0.3
numpy
opencv-python
sklearn