#MODLE_FILE = "/home/yonatan/trendi/yonatan/resnet_50_gender_by_face/ResNet-50-deploy.prototxt"
MODLE_FILE = "/data/production/caffemodels_and_protos/gender/ResNet-50-deploy.prototxt"
#PRETRAINED = "/home/yonatan/genderator_caffemodels/caffe_resnet50_snapshot_sgd_gender_by_face_iter_5000.caffemodel"
PRETRAINED = "/data/production/caffemodels_and_protos/gender/caffe_resnet50_snapshot_sgd_gender_by_face_iter_5000.caffemodel"
caffe.set_mode_gpu()
image_dims = [224, 224]
mean, input_scale = np.array([120, 120, 120]), None
channel_swap = [2, 1, 0]
raw_scale = 255.0
# Make classifier.
classifier = yonatan_classifier.Classifier(MODLE_FILE,
PRETRAINED,
image_dims=image_dims,
mean=mean,
input_scale=input_scale,
raw_scale=raw_scale,
channel_swap=channel_swap)
print "Done initializing!"
def cv2_image_to_caffe(image):
print('img size in cv2_img_to_caffe ' + str(image.shape))
return skimage.img_as_float(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).astype(
np.float32)
def theDetector(url_or_np_array, face_coordinates):
def genderator(image):
#input_image = sys.argv[1]
input_image = image
MODLE_FILE = "/home/yonatan/trendi/yonatan/Alexnet_deploy.prototxt"
PRETRAINED = "/home/yonatan/alexnet_imdb_first_try/caffe_alexnet_train_faces_iter_10000.caffemodel"
caffe.set_mode_gpu()
image_dims = [115, 115]
mean, input_scale = None, None
channel_swap = [2, 1, 0]
raw_scale = 255.0
ext = 'jpg'
# Make classifier.
classifier = yonatan_classifier.Classifier(MODLE_FILE,
PRETRAINED,
image_dims=image_dims,
mean=mean,
input_scale=input_scale,
raw_scale=raw_scale,
channel_swap=channel_swap)
# Load numpy array (.npy), directory glob (*.jpg), or image file.
input_file = os.path.expanduser(input_image)
if input_file.endswith('npy'):
print("Loading file: %s" % input_file)
inputs = np.load(input_file)
elif os.path.isdir(input_file):
print("Loading folder: %s" % input_file)
inputs = [
caffe.io.load_image(im_f)
for im_f in glob.glob(input_file + '/*.' + ext)
]
else:
print("Loading file: %s" % input_file)
inputs = [caffe.io.load_image(input_file)]
print("Classifying %d inputs." % len(inputs))
# Classify.
start = time.time()
predictions = classifier.predict(inputs)
print("Done in %.2f s." % (time.time() - start))
# making the predictions -> precentage
sum = predictions[0][0] + predictions[0][1]
#predictions[0][0] = predictions[0][0] / sum
#predictions[0][1] = predictions[0][1] / sum
if predictions[0][0] > predictions[0][1]:
print "it's a boy!"
else:
print "it's a girl!"
#print predictions
#print np.array(inputs).shape
#predictions_array = predictions
#text_file = open("face_testing.txt", "a")
#text_file.write("predictions: %s sum: %f\n" % (np.array2string(predictions, separator=', '), sum))
#text_file.flush()
return predictions
