Update gender_detection_test.py

src/gender_detection_test.py

@@ -1,42 +1 @@
-import unittest
-import cv2
-from gender_detection import Gender
-from align import detect_face
-import tensorflow as tf
-import numpy as np
 
-class GenderTestCase(unittest.TestCase):
-	def test_evaluate(self):
-		self.gn = Gender()
-		self.img = cv2.imread("/Users/amir/PycharmProjects/person_recognition/dataset/train/0004.0001.jpg")
-		self.model = self.gn.model
-		gpu_memory_fraction = 1.0
-		minsize = 50
-		threshold = [0.6, 0.6, 0.7]
-		factor = 0.709
-		print('Creating networks and loading parameters')
-		with tf.Graph().as_default():
-			gpu_options = tf.GPUOptions(
-				per_process_gpu_memory_fraction=gpu_memory_fraction)
-			sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
-			with sess.as_default():
-				pnet, rnet, onet = detect_face.create_mtcnn(
-					sess, None)
-		bounding_boxes, acc = detect_face.detect_face(
-			self.img, minsize, pnet,
-			rnet, onet, threshold, factor)
-		print self.img
-		(startx, starty) = int (bounding_boxes[0][0]), int (bounding_boxes[0][1])
-		(endx, endy) = int (bounding_boxes[0][2]), int(bounding_boxes[0][3])
-		print startx , starty , endx, endy
-		img= np.copy(self.img[starty:endy,startx:endx])
-		print img
-		label= self.gn.m_detector(img)
-		print("label : {}".format( label))
-		condition = label == "woman"
-		self.assertEqual(condition, True)
-		return label
-
-
-if __name__ == '__main__':
-	unittest.main()