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import numpy as np
import cv2
import keras
import os
import matplotlib.pyplot as plt
import time
# bald_path = "/home/julien/Downloads/archive (1)/Dataset/Test/Bald"
# notbald_path = "/home/julien/Downloads/archive (1)/Dataset/Test/NotBald"
def predict(im):
# im_names = os.listdirpath)
im_names= [im]
ims = np.zeros((len(im_names), 64, 64, 3))
for i, f in enumerate(im_names):
# img = cv2.imread(os.path.join(path, f))
# img = im
img = cv2.imread(f)
data = cv2.resize(img, (64, 64))
data = cv2.cvtColor(data, cv2.COLOR_BGR2RGB)
ims[i] = np.reshape(data, (1, 64, 64, 3)) / 255.0
print(ims.shape)
res = model.predict(ims)
"""
for i in range(len(im_names)):
print('[没秃]' if res[i][0] <0.3 else '[秃了]', 'Bald:',res[i][0])
"""
return res
model = keras.models.load_model('models/bald_classifity.h5')
"""
bald_res = predict(bald_path)
notbald_res = predict(notbald_path)
print(notbald_res.shape)
print(notbald_res[:,0])
print(f"Bald mean : {np.mean(bald_res[:, 0])}")
print(f"Not Bald mean : {np.mean(notbald_res[:, 0])}")
print(bald_res[:, 0]>0.3)
print(f"Accuracy : {(np.sum(bald_res[:, 0]>0.3) + np.sum(notbald_res[:, 0]<0.3))/(len(bald_res)+len(notbald_res))}")
"""
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