def simple_inference_with_color_filters(model,
session,
train_set_vectors,
uploaded_image_path,
color_vectors,
image_size,
distance='hamming'):
'''
Doing simple inference for single uploaded image.
:param model: CNN model
:param session: tf.Session, restored session
:param train_set_vectors: loaded training set vectors
:param uploaded_image_path: string, path to the uploaded image
:param color_vectors: loaded training set color features vectors
:param image_size: tuple, single image (height, width)
:param dsitance: string, type of distance to be used,
this parameter is used to choose a way how to prepare vectors
'''
image = image_loader(uploaded_image_path, image_size)
####################################################
## Calculating color histogram of the query image ##
channels = cv2.split(image)
features = []
for chan in channels:
hist = cv2.calcHist([chan], [0], None, [256], [0, 256])
features.append(hist)
color_features = np.vstack(features).T
####################################################
feed_dict = {model.inputs:[image], model.dropout_rate:0.0}
dense_2_features, dense_4_features = session.run([model.dense_2_features, model.dense_4_features], feed_dict=feed_dict)
closest_ids = None
if distance == 'hamming':
dense_2_features = np.where(dense_2_features < 0.5, 0, 1)
dense_4_features = np.where(dense_4_features < 0.5, 0, 1)
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = hamming_distance(train_set_vectors, uploaded_image_vector)
#Comparing color features between query image and closest images selected by the model
closest_ids = compare_color(np.array(color_vectors)[closest_ids], color_features, closest_ids)
elif distance == 'cosine':
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = cosine_distance(train_set_vectors, uploaded_image_vector)
#Comparing color features between query image and closest images selected by the model
closest_ids = compare_color(np.array(color_vectors)[closest_ids], color_features, closest_ids)
return closest_ids
def simple_inference_with_color_filters(model,
session,
train_set_vectors,
uploaded_image_path,
color_vectors,
image_size,
distance='hamming'):
image = image_loader(uploaded_image_path, image_size)
####################################################
## Calculating color histogram of the query image ##
channels = cv.split(image)
features = []
for chan in channels:
hist = cv.calcHist([chan], [0], None, [256], [0, 256])
features.append(hist)
color_features = np.vstack(features).T
####################################################
feed_dict = {model.inputs: [image], model.dropout_rate: 0.0}
dense_2_features, dense_4_features = session.run(
[model.dense_2_features, model.dense_4_features], feed_dict=feed_dict)
closest_ids = None
if distance == 'hamming':
dense_2_features = np.where(dense_2_features < 0.5, 0, 1)
dense_4_features = np.where(dense_4_features < 0.5, 0, 1)
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = hamming_distance(train_set_vectors,
uploaded_image_vector)
# Comparing color features between query image and closest images selected by the model
closest_ids = compare_color(
np.array(color_vectors)[closest_ids], color_features, closest_ids)
elif distance == 'cosine':
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = cosine_distance(train_set_vectors, uploaded_image_vector)
# Comparing color features between query image and closest images selected by the model
closest_ids = compare_color(
np.array(color_vectors)[closest_ids], color_features, closest_ids)
return closest_ids
def simple_inference(client,
train_set_vectors,
uploaded_image_path,
image_size,
distance='hamming'):
'''
Doing simple inference for single uploaded image.
:param client: Tensorflow serving client
:param train_set_vectors: loaded training set vectors
:param uploaded_image_path: string, path to the uploaded image
:param image_size: tuple, single image (height, width)
:param dsitance: string, type of distance to be used,
this parameter is used to choose a way how to prepare vectors
'''
image = image_loader(uploaded_image_path, image_size)
#Define model inputs
req_data = [{
'in_tensor_name': 'images:0',
'in_tensor_dtype': 'DT_FLOAT',
'data': np.array([image]).astype(float)
}, {
'in_tensor_name': 'drop:0',
'in_tensor_dtype': 'DT_FLOAT',
'data': 0.0
}]
#Send request data and get model predictions
dense_2_features, dense_4_features = client.predict(req_data)
closest_ids = None
if distance == 'hamming':
dense_2_features = np.where(dense_2_features < 0.5, 0, 1)
dense_4_features = np.where(dense_4_features < 0.5, 0, 1)
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = hamming_distance(train_set_vectors,
uploaded_image_vector)
elif distance == 'cosine':
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = cosine_distance(train_set_vectors, uploaded_image_vector)
return closest_ids
def simple_inference(model,
session,
train_set_vectors,
uploaded_image_path,
image_size,
distance='hamming'):
'''
Doing simple inference for single uploaded image.
:param model: CNN model
:param session: tf.Session, restored session
:param train_set_vectors: loaded training set vectors
:param uploaded_image_path: string, path to the uploaded image
:param image_size: tuple, single image (height, width)
:param dsitance: string, type of distance to be used,
this parameter is used to choose a way how to prepare vectors
'''
image = image_loader(uploaded_image_path, image_size)
feed_dict = {model.inputs: [image], model.dropout_rate: 0.0}
dense_2_features, dense_4_features = session.run(
[model.dense_2_features, model.dense_4_features], feed_dict=feed_dict)
closest_ids = None
if distance == 'hamming':
dense_2_features = np.where(dense_2_features < 0.5, 0, 1)
dense_4_features = np.where(dense_4_features < 0.5, 0, 1)
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = hamming_distance(train_set_vectors,
uploaded_image_vector)
elif distance == 'cosine':
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = cosine_distance(train_set_vectors, uploaded_image_vector)
return closest_ids
def simple_inference(
model,
session,
train_set_vectors,
uploaded_image_path, # string, path to the uploaded image
image_size,
distance='hamming'):
image = image_loader(uploaded_image_path, image_size)
channels = cv.split(image)
features = []
for chan in channels:
hist = cv.calcHist([chan], [0], None, [256], [0, 256])
features.append(hist)
color_features = np.vstack(features).T
feed_dict = {model.inputs: [image], model.dropout_rate: 0.0}
dense_2_features, dense_4_features = session.run(
[model.dense_2_features, model.dense_4_features], feed_dict=feed_dict)
closest_ids = None
if distance == 'hamming':
dense_2_features = np.where(dense_2_features < 0.5, 0, 1)
dense_4_features = np.where(dense_4_features < 0.5, 0, 1)
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = hamming_distance(train_set_vectors,
uploaded_image_vector)
elif distance == 'cosine':
uploaded_image_vector = np.hstack((dense_2_features, dense_4_features))
closest_ids = cosine_distance(train_set_vectors, uploaded_image_vector)
return closest_ids
