added predict

models/bald_classifity.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:83403b2aab2884f2ff3ed409a19017a6ece437c376897fc2c14267efdeefc4bd
+size 1760072

predict.py

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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('/home/julien/Documents/bald_classification/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))}")
+"""