def predict_likeliness(classifier, sess, default_graph, filename):
img = person_detector.get_person(filename, sess, default_graph)
img = img.convert('L')
img.save(filename, "jpeg")
certainty = classifier.classify(filename)
pos = certainty["positive"]
return pos
def predict_likeliness(classifier, sess, p, default_graph):
ratings = []
images = p['images']
for image in images:
req = requests.get(image)
tmp_filename = f"images/tmp/run.jpg"
if req.status_code == 200:
with open(tmp_filename, "wb") as f:
f.write(req.content)
#multiprocessing.Process(target=person_detector.get_person, args(tmp_filename, sess))
img = person_detector.get_person(tmp_filename, sess, default_graph)
if img:
img = img.convert('L')
img.save(tmp_filename, "jpeg")
certainty = classifier.classify(tmp_filename)
pos = certainty["positive"]
ratings.append(pos)
ratings.sort(reverse=True)
ratings = ratings[:5]
if len(ratings) == 0:
return 0.001
try:
return ratings[0] * 0.6 + sum(ratings[1:]) / len(ratings[1:]) * 0.4
except ZeroDivisionError:
return ratings[0] * 0.6
def predict_likeliness(self, classifier, sess):
ratings = []
if not len(self.images):
print('no image')
return 0.01, None
for image in self.images:
req = requests.get(image, stream=True)
tmp_filename = f"./images/tmp/run.jpg"
if req.status_code == 200:
with open(tmp_filename, "wb") as f:
f.write(req.content)
img = person_detector.get_person(tmp_filename, sess)
if img != None:
img = img.convert('L')
img.save(tmp_filename, "jpeg")
certainty = classifier.classify(tmp_filename)
pos = certainty["positive"]
ratings.append(pos)
if len(ratings) == 0:
print('person detector failed')
return 0.02, None
if len(ratings) == 1:
# print('len(ratings) = 1')
return max(ratings[0], 0.03), 0
best_pic_id = np.argmin(ratings)
ratings.sort(reverse=True)
if len(ratings) > 1:
ratings = ratings[:3]
return max(
ratings[0] * 0.6 + sum(ratings[1:]) / len(ratings[1:]) * 0.4,
0.04), best_pic_id
def predict_likeliness(self, classifier, sess):
ratings = []
for image in self.images:
req = requests.get(image, stream=True)
tmp_filename = f"./images/tmp/run.jpg"
if req.status_code == 200:
with open(tmp_filename, "wb") as f:
f.write(req.content)
img = person_detector.get_person(tmp_filename, sess)
if img:
img = img.convert('L')
img.save(tmp_filename, "jpeg")
certainty = classifier.classify(tmp_filename)
pos = certainty["positive"]
ratings.append(pos)
ratings.sort(reverse=True)
ratings = ratings[:5]
if len(ratings) == 0:
return 0.001
return ratings[0] * 0.6 + sum(ratings[1:]) / len(ratings[1:]) * 0.4
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)
img = person_detector.get_person(old_filename, sess)
if not img:
continue
img = img.convert('L')
img.save(new_filename, "jpeg")
for pos in positive_images:
old_filename = IMAGE_FOLDER + "/" + pos
new_filename = POS_FOLDER + "/" + pos[:-5] + ".jpg"
if not os.path.isfile(new_filename):
print(">> Moving positive image: " + pos)
img = person_detector.get_person(old_filename, sess)
if not img:
continue