def auto_like(self):
while True:
sleep(10)
try:
rawURL = self.get_image_path()
image_url = self.get_url(rawURL)
print(image_url)
user['photos'].append(image_url)
print(user['photos'])
person1 = person.Person(user)
detection_graph = person_detector.open_graph()
with detection_graph.as_default():
with tf.Session() as sess:
classifier = Classifier(
graph="./tf/training_output/retrained_graph.pb",
labels="./tf/training_output/retrained_labels.txt")
score = person1.predict_likeliness(classifier, sess)
print(score)
if (score[0] <= 0.6):
self.dislike()
print("Nope")
else:
self.like()
print("YES")
user['photos'].pop()
except Exception:
try:
self.close_pop_up()
except Exception:
try:
self.match_pop_up()
except Exception:
try:
self.window_pop_up()
except Exception:
# self.out_of_like_pop_up()
break
import person_detector
import tensorflow as tf
command = """
python retrain.py --bottleneck_dir=tf/training_data/bottlenecks --model_dir=tf/training_data/inception --summaries_dir=tf/training_data/summaries/basic --output_graph=tf/training_output/retrained_graph.pb --output_labels=tf/training_output/retrained_labels.txt --image_dir=./images/classified --how_many_training_steps=50000 --testing_percentage=20 --learning_rate=0.001
"""
IMAGE_FOLDER = "./images/unclassified"
POS_FOLDER = "./images/classified/positive"
NEG_FOLDER = "./images/classified/negative"
LOVOO_FOLDER = "./images/lovoo"
if __name__ == "__main__":
detection_graph = person_detector.open_graph()
images = [f for f in os.listdir(IMAGE_FOLDER) if os.path.isfile(os.path.join(IMAGE_FOLDER, f))]
positive_images = filter(lambda image: (image.startswith("1_")), images)
negative_images = filter(lambda image: (image.startswith("0_")), images)
lovoo_images = [f for f in os.listdir(LOVOO_FOLDER) if os.path.isfile(os.path.join(LOVOO_FOLDER, f))]
with detection_graph.as_default():
with tf.Session() as sess:
for pos in lovoo_images:
old_filename = LOVOO_FOLDER + "/" + pos
new_filename = POS_FOLDER + "/" + pos[:-5] + ".jpg"
if not os.path.isfile(new_filename):
print(">> Moving positive image: " + pos)