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classifier_5_v2.py
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classifier_5_v2.py
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import warnings
warnings.filterwarnings("ignore", message="numpy.dtype size changed")
warnings.filterwarnings("ignore", message="numpy.ufunc size changed")
import cv2, numpy as np, random, os, pickle, keras, itertools
from mlp import train
from PCA import pca
from util_ import get_boundry_img_matrix, get_files
def make_sets(inputs, out, percent):
if len(inputs) != len(out): print("Error input size not equal to output size !!!")
x_train = []
y_train = []
x_test = []
y_test = []
rang = range(len(inputs))
random.shuffle(rang)
for i in rang:
if random.random() < percent:
x_test.append(inputs[i])
y_test.append(out[i])
else:
x_train.append(inputs[i])
y_train.append(out[i])
return x_train, y_train, x_test, y_test
############### constant ################
MEAN_AREA =0
PERIMETER = 1
R = 2
B = 3
G = 4
EIGEN_VALUE_1 = 5
EIGEN_VALUE_2 = 6
ECCENTRICITY = 7
features = {0:'MEAN_AREA', 1:'PERIMETER', 2:'R', 3:'B', 4:'G', 5:'EIGEN_VALUE_1', 6:'EIGEN_VALUE_2', 7:'ECCENTRICITY', 8:'NUMBER_GRAIN',}
#########################################
data_dir = 'dataset5_dep_on_4/'
result_dir = 'weights_results_5out/'
if __name__ == "__main__":
ftrain = []
ftest = []
grain_class = {
'damaged_grain': 0,
'foreign': 1,
'grain': 2,
'broken_grain': 3,
'grain_cover': 4
}
# extracting features of grains
feat_data = result_dir + 'grain_feature_.pkl'
if os.path.isfile(feat_data):
ftrain, y_train, ftest, y_test = pickle.load(open(feat_data, 'rb'), encoding="bytes")
else:
grain_particles = {
'damaged_grain' : data_dir + 'damaged_grain',
'foreign' : data_dir + 'foreign_particles',
'grain' : data_dir + 'grain',
'broken_grain' : data_dir + 'grain_broken',
'grain_cover' : data_dir + 'grain_covered'
}
grain_partical_list = {k:get_files(v) for k,v in grain_particles.items()}
# impurity_list = get_files(impure)
# grain_list = get_files(grain)
all_partical = []
for i in grain_partical_list: all_partical += grain_partical_list[i]
partical_classes = []
for i in grain_partical_list:
a = np.zeros(len(grain_class))
a[grain_class[i]] = 1
partical_classes += [a for j in range(len(grain_partical_list[i]))]
# out = [[1, 0] for i in range(len(grain_list))] + [[0, 1] for i in range(len(impurity_list))]
x_train, y_train, x_test, y_test = make_sets(all_partical, partical_classes, 0.3)
print(' Number of damaged_grain: ', len(grain_partical_list['damaged_grain']))
print(' Number of foreign: ', len(grain_partical_list['foreign']))
print(' Number of grain: ', len(grain_partical_list['grain']))
print(' Number of broken_grain: ', len(grain_partical_list['broken_grain']))
print(' Number of grain_cover: ', len(grain_partical_list['grain_cover']))
print("Total of sample: ", len(all_partical))
xgtrain = []
xctrain = []
for g in x_train:
img = cv2.imread(g, cv2.IMREAD_COLOR)
xctrain.append(img)
xgtrain.append(img[:, :, 2])
xgtest = []
xctest = []
for i in x_test:
img = cv2.imread(i, cv2.IMREAD_COLOR)
xctest.append(img)
xgtest.append(img[:, :, 2])
for gi in range(len(xctrain)):
gcolor = xctrain[gi]
ggray = xgtrain[gi]
h, w = ggray.shape
thresh = np.array([[255 if pixel > 0 else 0 for pixel in row] for row in ggray])
b = np.array(get_boundry_img_matrix(thresh, bval=1), dtype=np.float32)
perameter = np.sum(b)/(h*w)
area = np.sum(np.sum([[1.0 for j in range(w) if ggray[i,j]] for i in range(h)]))
mean_area = area/(h*w)
r, b, g = np.sum([gcolor[i,j] for j in range(gcolor.shape[1]) for i in range(gcolor.shape[0])], axis=0)/(area*256)
_,_,eigen_value = pca(ggray)
eccentricity = eigen_value[0]/eigen_value[1]
l = [mean_area, perameter, r,b,g,eigen_value[0],eigen_value[1], eccentricity]
ftrain.append(np.array(l))
for gi in range(len(xctest)):
gcolor = xctest[gi]
ggray = xgtest[gi]
h, w = ggray.shape
thresh = np.array([[255 if pixel > 0 else 0 for pixel in row] for row in ggray])
b = np.array(get_boundry_img_matrix(thresh, bval=1), dtype=np.float32)
perameter = np.sum(b)/(h*w)
area = np.sum(np.sum([[1.0 for j in range(w) if ggray[i,j]] for i in range(h)]))
mean_area = area / (h * w)
r, b, g = np.sum([gcolor[i, j] for j in range(gcolor.shape[1]) for i in range(gcolor.shape[0])], axis=0) / (area*256)
_, _, eigen_value = pca(ggray)
eccentricity = eigen_value[0] / eigen_value[1]
l = [mean_area, perameter, r, b, g, eigen_value[0], eigen_value[1], eccentricity]
ftest.append(l)
pickle.dump([ftrain, y_train, ftest, y_test], open(result_dir + 'grain_feature.pkl', 'wb'))
print("Total of sample for training :", len(ftrain))
print("Total of sample for testing :", len(ftest))
# MLP
fd = open(result_dir + 'Test_results.txt','a')
# m = [MEAN_AREA, PERIMETER, R, B, G]
# n = [[EIGEN_VALUE_1, EIGEN_VALUE_2], [ECCENTRICITY], [NUMBER_GRAIN]]
print("Trainning linear MLP...")
# allComb = [list(j) for i in range(1,len(n)+1) for j in itertools.combinations(n, i)]
allComb = [[MEAN_AREA, PERIMETER, R, B, G, EIGEN_VALUE_1, EIGEN_VALUE_2, ECCENTRICITY]]
for feat in allComb:
# print(n, np.array(ftrain)[:, n].shape)
# feat = [i for i in m]
# for i in n: feat += i
print('Paremeters :', [features[i] for i in feat]," ##### Number of classes :", [i for i in grain_class])
modleFile = result_dir + 'weights_'+''.join([str(i) for i in feat])+'.h5'
if os.path.isfile(modleFile):
model = keras.models.load_model(modleFile)
else:
model = train(np.array(ftrain)[:,feat], np.array(y_train), modelf=modleFile)
model.save(modleFile)
print("Model file is saved !!")
score = model.evaluate(np.array(ftest)[:,feat], np.array(y_test))
print('MLP Test loss:', score[0])
print('MLP Test accuracy:', score[1])
fd.write("Featrues: "+str([features[i] for i in feat])+'\n')
fd.write('MLP Test loss: %f\n'%(score[0]))
fd.write('MLP Test accuracy: %f\n\n'%(score[1]))