def search():
# Dữ liệu đầu vào
file = request.files['file']
file.save('./' + file.filename)
features = mongo.db.features
# Đọc ảnh input
image = cv2.imread('./' + file.filename)
color_feature = Descriptor.color_history(image, 8)
lpb_feature = Descriptor.lpb_history(image, 24, 8)
hog_feature = Descriptor.hog(image, 10, 2, 9)
feature = np.hstack([color_feature, lpb_feature, hog_feature])
db_features = features.find()
results_euclidean = {}
results_cosine = {}
for db_feature in db_features:
dist_euclidean = euclidean(np.asarray(db_feature['feature']), feature)
results_euclidean[db_feature['path']] = dist_euclidean
dist_cosine = cosine(np.asarray(db_feature['feature']), feature)
results_cosine[db_feature['path']] = dist_cosine
results_euclidean = sorted([(d, n) for n, d in results_euclidean.items()])
results_cosine = sorted([(d, n) for n, d in results_cosine.items()])
return jsonify({
'result_euclidean': results_euclidean,
'result_cosine': results_cosine
})
def __init__(self, img, x, y, w, h):
self.id = uuid.uuid1()
self.descriptor = Descriptor(img, x, y, w, h)
self.position = Position(x, y, w, h)
self.last_seen = time.time()
self.color = (random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255))
self.is_followed = False
class WSHandler(tornado.websocket.WebSocketHandler):
def open(self):
self.descriptor = Descriptor(self)
def on_message(self, message):
print message
self.descriptor.input(message)
def on_close(self):
self.descriptor.disconnect()
def main():
image_id = input("Enter image ID: ")
constants_dict = read_json()
read_path = constants_dict["READ_PATH"]
files = os.listdir(read_path)
file = files[files.index("{}.jpg".format(image_id))]
img = cv2.imread(read_path + file)
desc = Descriptor(img)
print(desc.sift())
print(desc.lbp())
def process_files(path,
feature_model,
dimension_reduction,
filtered_image_ids=None):
files = os.listdir(path)
ids, x = [], []
for file in files:
if not filtered_image_ids or (filtered_image_ids and file.replace(
".jpg", "") in filtered_image_ids):
print("Reading file: {}".format(file))
image = cv2.imread("{}{}".format(path, file))
feature_descriptor = Descriptor(
image, feature_model, dimension_reduction).feature_descriptor
ids.append(file.replace(".jpg", ""))
x.append(feature_descriptor)
if DescriptorType(feature_model).check_sift():
"""
For SIFT, we flatten the image descriptor array into an array of keypoints.
We return an extra list (pos) representing the number of keypoints for each image.
This is done to extract the feature descriptors (after dimensionality reduction) of
each image correctly while inserting into the DB.
"""
sift_x, pos = x[0], [x[0].shape[0]]
for i in range(1, len(x)):
pos.append(x[i].shape[0])
sift_x = np.vstack((sift_x, x[i]))
return sift_x, ids, pos
"""
For all other feature descriptors, return only the ids and descriptor array.
"""
return np.array(x), ids
def loadClassifier(self, filepath=None, classifier_data=None):
"""
Load a classifier trained by the functions in train.py. Either a dict
(classifier_data) or pickled file (filepath) may be supplied.
"""
if filepath is not None:
filepath = os.path.abspath(filepath)
if not os.path.isfile(filepath):
raise FileNotFoundError("File " + filepath +
" does not exist.")
classifier_data = pickle.load(open(filepath, "rb"))
else:
classifier_data = classifier_data
if classifier_data is None:
raise ValueError("Invalid classifier data supplied.")
self.classifier = classifier_data["classifier"]
self.scaler = classifier_data["scaler"]
self.cv_color_const = classifier_data["cv_color_const"]
self.channels = classifier_data["channels"]
# Simply loading the descriptor from the dict with
# self.descriptor = classifier_data["descriptor"]
# produces an error. Thus, we instantiate a new descriptor object
# using the same parameters on which the classifier was trained.
self.descriptor = Descriptor(
hog_features=classifier_data["hog_features"],
hist_features=classifier_data["hist_features"],
spatial_features=classifier_data["spatial_features"],
hog_lib=classifier_data["hog_lib"],
size=classifier_data["size"],
hog_bins=classifier_data["hog_bins"],
pix_per_cell=classifier_data["pix_per_cell"],
cells_per_block=classifier_data["cells_per_block"],
block_stride=classifier_data["block_stride"],
block_norm=classifier_data["block_norm"],
transform_sqrt=classifier_data["transform_sqrt"],
signed_gradient=classifier_data["signed_gradient"],
hist_bins=classifier_data["hist_bins"],
spatial_size=classifier_data["spatial_size"])
return self
def extract_features(arguments, dataset_list):
PATH = str(arguments.path)
DATASET = str(arguments.file)
DESCRIPTOR = str(arguments.desc)
IMG_WIDTH = int(arguments.width)
IMG_HEIGHT = int(arguments.height)
KNOWN_SET_SIZE = float(arguments.known_set_size)
TRAIN_SET_SIZE = float(arguments.train_set_size)
matrix_x = []
matrix_y = []
matrix_z = []
vgg_model = None
if DESCRIPTOR == 'df':
from vggface import VGGFace
vgg_model = VGGFace()
counterA = 0
for sample in dataset_list:
sample_path = sample[0]
sample_name = sample[1]
subject_path = PATH + sample_path
subject_image = cv.imread(subject_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
subject_image = cv.resize(subject_image, (IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(subject_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(subject_image,
vgg_model,
layer_name='fc6')
matrix_x.append(feature_vector)
matrix_y.append(sample_name)
matrix_z.append(sample_path)
counterA += 1
print(counterA, sample_path, sample_name)
return matrix_z, matrix_y, matrix_x
def process():
dirs = os.listdir(path)
features = mongo.db.features
for file in dirs:
# đọc từng file
image = cv2.imread(path + '/' + file)
# Vẽ sơ đồ histogram của 3 đặc trưng :
# đặc trưng màu sắc,
color_feature = Descriptor.color_history(image, 8)
# đặc trưng kết cấu ảnh(lpb),
lpb_feature = Descriptor.lpb_history(image, 24, 8)
# đặc trưng HOG
hog_feature = Descriptor.hog(image, 10, 2, 9)
feature = list(np.hstack([color_feature, lpb_feature, hog_feature]))
id = features.insert_one({
'path': path + '/' + file,
'feature': feature
})
return 'done'
def extract_features(arguments, dataset_list):
PATH = str(arguments.path)
DATASET = str(arguments.file)
DESCRIPTOR = str(arguments.desc)
IMG_WIDTH = int(arguments.width)
IMG_HEIGHT = int(arguments.height)
matrix_x = []
matrix_y = []
matrix_z = []
vgg_model = None
if DESCRIPTOR == 'df':
from vggface import VGGFace
vgg_model = VGGFace()
counterA = 0
for sample in dataset_list:
try:
sample_path = sample[0]
sample_name = sample[1]
subject_path = PATH + sample_path
subject_image = cv.imread(subject_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
subject_image = cv.resize(subject_image, (IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(subject_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(subject_image, vgg_model, layer_name='fc6')
matrix_x.append(feature_vector)
matrix_y.append(sample_name)
matrix_z.append(sample_path)
print(counterA, sample_path, sample_name, len(feature_vector))
except Exception, e:
print(counterA, sample_path + ' not loaded', str(e))
counterA += 1
def load_model(self, file_path):
file_path = os.path.abspath(file_path)
if not os.path.isfile(file_path):
raise FileNotFoundError("File " + file_path + " not found.")
model_data = pickle.load(open(file_path, "rb"))
self.model = model_data["model"]
self.scaler = model_data["scaler"]
self.color_const = model_data["color_const"]
self.channels = model_data["channels"]
self.descriptor = Descriptor(
hog=model_data["hog"],
histogram=model_data["histogram"],
spatial=model_data["spatial"],
hog_size=model_data["hog_size"],
hog_bins=model_data["hog_bins"],
cell_size=model_data["cell_size"],
cells_per_block=model_data["cells_per_block"],
histogram_bins=model_data["histogram_bins"],
spatial_size=model_data["spatial_size"])
return self
def process_files(path, feature_model, dimension_reduction):
files = os.listdir(path)
ids, x = [], []
for file in files:
print("Reading file: {}".format(file))
image = cv2.imread("{}{}".format(path, file))
feature_descriptor = Descriptor(
image, feature_model, dimension_reduction
).feature_descriptor
ids.append(file.replace(".jpg", ""))
x.append(feature_descriptor)
return ids, np.array(x)
def parse_annotation_value(value):
"""
Convert the annotation from a type or instance, into a type
"""
# Value is either a type, or an instance that needs turning into a type
if isinstance(value, type):
return Descriptor.of(value)
elif isinstance(value, list):
assert type(value[0]) == type or type(type(value[0])) == type
return ProtectedList.of(value[0])
elif isinstance(value, tuple):
return Enum.of(*value)
else:
raise TypeError("Unable to parse annotation value {}".format(value))
def __init__(self, bus, index, characteristic):
Descriptor.__init__(self, bus, index, self.TEST_DESC_UUID,
['read', 'write'], characteristic)
def processFiles(pos_dir,
neg_dir,
recurse=False,
output_file=False,
output_filename=None,
color_space="bgr",
channels=[0, 1, 2],
hog_features=False,
hist_features=False,
spatial_features=False,
hog_lib="cv",
size=(64, 64),
hog_bins=9,
pix_per_cell=(8, 8),
cells_per_block=(2, 2),
block_stride=None,
block_norm="L1",
transform_sqrt=True,
signed_gradient=False,
hist_bins=16,
spatial_size=(16, 16)):
"""
Extract features from positive samples and negative samples.
Store feature vectors in a dict and optionally save to pickle file.
@param pos_dir (str): Path to directory containing positive samples.
@param neg_dir (str): Path to directory containing negative samples.
@param recurse (bool): Traverse directories recursively (else, top-level only).
@param output_file (bool): Save processed samples to file.
@param output_filename (str): Output file filename.
@param color_space (str): Color space conversion.
@param channels (list): Image channel indices to use.
For remaining arguments, refer to Descriptor class:
@see descriptor.Descriptor#__init__(...)
@return feature_data (dict): Lists of sample features split into training,
validation, test sets; scaler object; parameters used to
construct descriptor and process images.
NOTE: OpenCV HOGDescriptor currently only supports 1-channel and 3-channel
images, not 2-channel images.
"""
if not (hog_features or hist_features or spatial_features):
raise RuntimeError(
"No features selected (set hog_features=True, " +
"hist_features=True, and/or spatial_features=True.)")
pos_dir = os.path.abspath(pos_dir)
neg_dir = os.path.abspath(neg_dir)
if not os.path.isdir(pos_dir):
raise FileNotFoundError("Directory " + pos_dir + " does not exist.")
if not os.path.isdir(neg_dir):
raise FileNotFoundError("Directory " + neg_dir + " does not exist.")
print("Building file list...")
if recurse:
pos_files = [
os.path.join(rootdir, file)
for rootdir, _, files in os.walk(pos_dir) for file in files
]
neg_files = [
os.path.join(rootdir, file)
for rootdir, _, files in os.walk(neg_dir) for file in files
]
else:
pos_files = [
os.path.join(pos_dir, file) for file in os.listdir(pos_dir)
if os.path.isfile(os.path.join(pos_dir, file))
]
neg_files = [
os.path.join(neg_dir, file) for file in os.listdir(neg_dir)
if os.path.isfile(os.path.join(neg_dir, file))
]
print("{} positive files and {} negative files found.\n".format(
len(pos_files), len(neg_files)))
# Get color space information.
color_space = color_space.lower()
if color_space == "gray":
color_space_name = "grayscale"
cv_color_const = cv2.COLOR_BGR2GRAY
channels = [0]
elif color_space == "hls":
color_space_name = "HLS"
cv_color_const = cv2.COLOR_BGR2HLS
elif color_space == "hsv":
color_space_name = "HSV"
cv_color_const = cv2.COLOR_BGR2HSV
elif color_space == "lab":
color_space_name = "Lab"
cv_color_const = cv2.COLOR_BGR2Lab
elif color_space == "luv":
color_space_name = "Luv"
cv_color_const = cv2.COLOR_BGR2Luv
elif color_space == "ycrcb" or color_space == "ycc":
color_space_name = "YCrCb"
cv_color_const = cv2.COLOR_BGR2YCrCb
elif color_space == "yuv":
color_space_name = "YUV"
cv_color_const = cv2.COLOR_BGR2YUV
else:
color_space_name = "BGR"
cv_color_const = -1
# Get names of desired features.
features = [
feature_name for feature_name, feature_bool in
zip(["HOG", "color histogram", "spatial"],
[hog_features, hist_features, spatial_features])
if feature_bool == True
]
feature_str = features[0]
for feature_name in features[1:]:
feature_str += ", " + feature_name
# Get information about channel indices.
if len(channels) == 2 and hog_features and hog_lib == "cv":
warnings.warn("OpenCV HOG does not support 2-channel images",
RuntimeWarning)
channel_index_str = str(channels[0])
for ch_index in channels[1:]:
channel_index_str += ", {}".format(ch_index)
print("Converting images to " + color_space_name + " color space and " +
"extracting " + feature_str + " features from channel(s) " +
channel_index_str + ".\n")
# Store feature vectors for positive samples in list pos_features and
# for negative samples in neg_features.
pos_features = []
neg_features = []
start_time = time.time()
# Get feature descriptor object to call on each sample.
descriptor = Descriptor(hog_features=hog_features,
hist_features=hist_features,
spatial_features=spatial_features,
hog_lib=hog_lib,
size=size,
hog_bins=hog_bins,
pix_per_cell=pix_per_cell,
cells_per_block=cells_per_block,
block_stride=block_stride,
block_norm=block_norm,
transform_sqrt=transform_sqrt,
signed_gradient=signed_gradient,
hist_bins=hist_bins,
spatial_size=spatial_size)
# Iterate through files and extract features.
for i, filepath in enumerate(pos_files + neg_files):
image = cv2.imread(filepath)
if cv_color_const > -1:
image = cv2.cvtColor(image, cv_color_const)
if len(image.shape) > 2:
image = image[:, :, channels]
feature_vector = descriptor.getFeatureVector(image)
if i < len(pos_files):
pos_features.append(feature_vector)
else:
neg_features.append(feature_vector)
print("Features extracted from {} files in {:.1f} seconds\n".format(
len(pos_features) + len(neg_features),
time.time() - start_time))
# Store the length of the feature vector produced by the descriptor.
num_features = len(pos_features[0])
##TODO: Instantiate scaler and scale features.
scaler = StandardScaler()
scaler.fit(np.concatenate((pos_features, neg_features), axis=0))
pos_features = scaler.transform(pos_features)
neg_features = scaler.transform(neg_features)
##TODO: Randomize lists of feature vectors. Split 75/20/5 into training,
# validation, and test sets.
print(
"Shuffling samples into training, cross-validation, and test sets.\n")
random.shuffle(pos_features)
random.shuffle(neg_features)
# Use pos_train, pos_val, pos_test and neg_train, neg_val, neg_test to represent
# the Train, Validation and Test sets of Positive and Negtive sets.
pos_train, pos_val, pos_test = np.split(
pos_features,
[int(.75 * len(pos_features)),
int(.95 * len(pos_features))])
neg_train, neg_val, neg_test = np.split(
neg_features,
[int(.75 * len(neg_features)),
int(.95 * len(neg_features))])
# Store sample data and parameters in dict.
# Descriptor class object seems to produce errors when unpickling and
# has been commented out below. The descriptor will be re-instantiated
# by the Detector object later.
feature_data = {
"pos_train": pos_train,
"neg_train": neg_train,
"pos_val": pos_val,
"neg_val": neg_val,
"pos_test": pos_test,
"neg_test": neg_test,
#"descriptor": descriptor,
"scaler": scaler,
"hog_features": hog_features,
"hist_features": hist_features,
"spatial_features": spatial_features,
"color_space": color_space,
"cv_color_const": cv_color_const,
"channels": channels,
"hog_lib": hog_lib,
"size": size,
"hog_bins": hog_bins,
"pix_per_cell": pix_per_cell,
"cells_per_block": cells_per_block,
"block_stride": block_stride,
"block_norm": block_norm,
"transform_sqrt": transform_sqrt,
"signed_gradient": signed_gradient,
"hist_bins": hist_bins,
"spatial_size": spatial_size,
"num_features": num_features
}
# Pickle to file if desired.
if output_file:
if output_filename is None:
output_filename = (datetime.now().strftime("%Y%m%d%H%M") +
"_data.pkl")
pickle.dump(feature_data, open(output_filename, "wb"))
print(
"Sample and parameter data saved to {}\n".format(output_filename))
return feature_data
from descriptor import Descriptor
import glob
import cv2
import argparse
argument_parser = argparse.ArgumentParser()
argument_parser.add_argument('-d','--dataset', required = True,
help = 'Path to directory containing the dataset')
argument_parser.add_argument('-i','--index', required = True,
help = 'Path to save computed index')
args = vars(argument_parser.parse_args())
color_descriptor = Descriptor((8, 12, 3))
number_of_images = len(glob.glob('jpg/*.jpg'))
with open(args['index'], 'w') as output:
for image_path in glob.glob(args['dataset'] + '/*.jpg'):
image_id = image_path[image_path.rfind('/') + 1:]
image = cv2.imread(image_path)
features = color_descriptor.describe(image)
print('Computing features for image', image_id)
features = [str(f) for f in features]
output.write('%s, %s\n' % (image_id,','.join(features)))
print(str((number_of_images - 1)) + 'left')
number_of_images = number_of_images - 1
def __init__(self):
Descriptor.__init__(self)
def clustering(path, c):
mongo_url = "mongodb://localhost:27017/"
database_name = "mwdb_phase3"
lbld_collection_name = "labelled_hands"
unlbld_collection_name = "unlabelled_hands"
meta_collection_name = "metadata"
lbld_csv = "C:/Users/priya/Documents/images/Phase 3/phase3_sample_data/labelled_set1.csv"
unlabelled_csv = "C:/Users/priya/Documents/images/Phase 3/phase3_sample_data/Unlabelled/unlablled_set1.csv"
try:
connection = MongoClient(mongo_url)
database = connection[database_name]
lbld_collection = database[lbld_collection_name]
unlbld_collection = database[unlbld_collection_name]
meta_collection = database[meta_collection_name]
# storing labelled images
df = pd.read_csv(lbld_csv)
lbld_records = df.to_dict(orient='records')
lbld_collection.remove()
lbld_collection.insert_many(lbld_records)
# storing unlabelled images
df = pd.read_csv(unlabelled_csv)
unlbld_records = df.to_dict(orient='records')
unlbld_collection.remove()
unlbld_collection.insert_many(unlbld_records)
ids1, ids2, feature_vector1, feature_vector2, feature_vector3 = [], [], [], [], []
colors = ['red', 'blue', 'green', 'cyan', 'magenta']
markers = ['o', '<', 's', '+', 'v', '^', '.', '>', ',', 'd']
clust_labels = []
cent_labels = []
cluster = "Cluster "
cent = "Centroid "
for i in range(c):
clust_labels.append(cluster.join(str(i)))
cent_labels.append(cent.join(str(i)))
# extracting features
# dorsal
for subject in lbld_collection.find({"aspectOfHand": {"$regex": "dorsal"}}, {"imageName": 1}):
image_id = subject['imageName']
img_path = path + image_id
image = cv2.imread(img_path)
ids1.append(image_id.replace(".jpg", ""))
feature_descriptor = Descriptor(image, 1).feature_descriptor
# normalize features
features_norm = (feature_descriptor - feature_descriptor.min()) / (
feature_descriptor.max() - feature_descriptor.min())
feature_vector1.append(features_norm)
_, d_latent_semantics = LatentSymantics(
np.array(feature_vector1), 2, 1
).latent_symantics
# K means
centroids, prev_centroids, classes, X, centroid_norm, d_img_classes = [], [], [], [], [], []
max_iterations = 1
isOptimal = False
for i in range(c):
centroids.append(d_latent_semantics[i])
prev_centroids.append(d_latent_semantics[i])
while not isOptimal and max_iterations < 501:
d_distances = []
classes = []
d_img_classes = []
for i in range(c):
classes.append([])
d_img_classes.append([])
# Calculating clusters for each feature
for i in range(d_latent_semantics.shape[0]):
features = d_latent_semantics[i]
d_distances = [euclidean(features, centroid) for centroid in centroids]
classification = d_distances.index(min(d_distances))
classes[classification].append(features)
d_img_classes[classification].append(ids1[i])
# Recalculating centroids
for i in range(len(classes)):
centroids[i] = np.mean(classes[i], axis=0)
isOptimal = True
for i in range(len(centroids)):
if sum((centroids[i] - prev_centroids[i]) / prev_centroids[i] * 100.0) > tolerance:
isOptimal = False
break
prev_centroids[i] = centroids[i]
max_iterations += 1
# # Visualize clusters -- takes longer time to show so commented
# for i in range(c):
# plt.scatter(centroids[i][0], centroids[i][1], s=300, c="black", marker="x", label=cent_labels[i])
# for features in classes[i]:
# plt.scatter(features[0], features[1], color=colors[i], s=30, marker=markers[i], label=clust_labels[i])
# plt.show()
print "Dorsal CLusters: "
for i in range(len(d_img_classes)):
print ("Cluster %d: " % i)
print d_img_classes[i]
# ---------------------------------------------------------------------------------------------------------------------
# extracting features
# palmar
for subject in lbld_collection.find({"aspectOfHand": {"$regex": "palmar"}}, {"imageName": 1}):
image_id = subject['imageName']
img_path = path + image_id
image = cv2.imread(img_path)
ids2.append(image_id.replace(".jpg", ""));
# normalize features
feature_descriptor = Descriptor(image, 1).feature_descriptor
features_norm = (feature_descriptor - feature_descriptor.min()) / (
feature_descriptor.max() - feature_descriptor.min())
feature_vector2.append(features_norm)
_, p_latent_semantics = LatentSymantics(
np.array(feature_vector2), 2, 1
).latent_symantics
# K means
p_centroids, p_prev_centroids, p_classes, p_X, p_centroid_norm, p_img_classes = [], [], [], [], [], []
p_max_iterations = 1
p_isOptimal = False
for i in range(c):
p_centroids.append(p_latent_semantics[i])
p_prev_centroids.append(p_latent_semantics[i])
p_classes.append([])
p_img_classes.append([])
while not p_isOptimal and p_max_iterations < 501:
p_distances = []
p_classes = []
p_img_classes = []
for i in range(c):
p_classes.append([])
p_img_classes.append([])
# Calculating clusters for each feature
for i in range(p_latent_semantics.shape[0]):
features = p_latent_semantics[i]
p_distances = [euclidean(features, centroid) for centroid in p_centroids]
classification = p_distances.index(min(p_distances))
p_classes[classification].append(features)
p_img_classes[classification].append(ids2[i])
# Recalculating centroids
for i in range(len(p_classes)):
p_centroids[i] = np.mean(p_classes[i], axis=0)
p_isOptimal = True
for i in range(len(p_centroids)):
if sum((p_centroids[i] - p_prev_centroids[i]) / p_prev_centroids[i] * 100.0) > tolerance:
p_isOptimal = False
break
p_prev_centroids[i] = p_centroids[i]
p_max_iterations += 1
# # Visualize clusters -- takes longer time to show so commented
# for i in range(c):
# plt.scatter(p_centroids[i][0], p_centroids[i][1], s=130, marker="x")
# for features in p_classes[i]:
# plt.scatter(features[0], features[1], color=colors[i], s=30, marker=markers[i])
# plt.show()
print "Palmar CLusters: "
for i in range(len(p_img_classes)):
print ("Cluster %d" % i)
print p_img_classes[i]
# ----------------------------------------------------------------------------------------------------------------------
# Classification
# mean_dorsal = np.mean(centroids, axis=0)
# mean_palmar = np.mean(p_centroids, axis=0)
image_name = []
dorsal_cnt = 0
palmar_cnt = 0
d_cnt = 0
p_cnt = 0
for image_path in glob.glob(test_path):
image = cv2.imread(image_path)
# get filename
image_name.append(os.path.basename(image_path))
feature_descriptor = Descriptor(image, 1).feature_descriptor
# normalize features
features_norm = (feature_descriptor - feature_descriptor.min()) / (
feature_descriptor.max() - feature_descriptor.min())
feature_vector3.append(features_norm)
_, latent_semantics = LatentSymantics(np.array(feature_vector3), 2, 1).latent_symantics
for i in range(len(latent_semantics)):
ddistances = [euclidean(latent_semantics[i], centroid) for centroid in centroids]
pdistances = [euclidean(latent_semantics[i], centroid) for centroid in p_centroids]
subject_img = unlbld_collection.find_one({"imageName": image_name[i]}, {"aspectOfHand": 1})
if "dorsal" in subject_img['aspectOfHand']:
d_cnt += 1
else:
p_cnt += 1
if min(ddistances) < min(pdistances):
if "dorsal" in subject_img['aspectOfHand']:
dorsal_cnt += 1
print ("Image ID: %s, %s" % (image_name[i], "dorsal"))
else:
if "palmar" in subject_img['aspectOfHand']:
palmar_cnt += 1
print ("Image ID: %s, %s" % (image_name[i], "palmar"))
print ("Dorsal Accuracy %d" % ((dorsal_cnt*100)/d_cnt))
print ("Palmar Accuracy %d" % ((palmar_cnt*100)/p_cnt))
except Exception as e:
traceback.print_exc()
print("Connection refused... ")
def __init__(self):
Descriptor.__init__(self)
def helper(self,feature_model, dimension_reduction, k):
unlabelled_path = "C:/Users/himan/OneDrive/Desktop/MWDB/phase3_sample_data/Unlabelled/Set 1/"
files = os.listdir(unlabelled_path)
path, pos = Config().read_path(), None
descriptor_type = DescriptorType(feature_model).descriptor_type
symantics_type = LatentSymanticsType(dimension_reduction).symantics_type
label, value, complementary_value = ("dorsal", 1, 0)
unlabelled_image_feature_vector = []
unlabelled_image_ids = []
for i, file in enumerate(files):
print(file)
image = cv2.imread("{}{}".format(unlabelled_path, file))
image_feature_vector = Descriptor(
image, feature_model, dimension_reduction
).feature_descriptor
unlabelled_image_feature_vector.append(image_feature_vector)
unlabelled_image_ids.append(file)
label_filtered_image_ids = [
item["image_id"]
for item in Database().retrieve_metadata_with_labels(label, value)
]
complementary_label_filtered_image_ids = [
item["image_id"]
for item in Database().retrieve_metadata_with_labels(label, complementary_value)
]
if DescriptorType(feature_model).check_sift():
label_feature_vector, label_ids, label_pos = functions_phase2.process_files(
path, feature_model, dimension_reduction, label_filtered_image_ids
)
complementary_label_feature_vector, complementary_label_ids, complementary_label_pos = functions_phase2.process_files(
path,
feature_model,
dimension_reduction,
complementary_label_filtered_image_ids,
)
feature_vector = np.concatenate(
(
label_feature_vector,
complementary_label_feature_vector,
unlabelled_image_feature_vector,
)
)
# pos = label_pos + complementary_label_pos + [image_feature_vector.shape[0]]
else:
label_feature_vector, label_ids = functions_phase2.process_files(
path, feature_model, dimension_reduction, label_filtered_image_ids
)
complementary_label_feature_vector, complementary_label_ids = functions_phase2.process_files(
path,
feature_model,
dimension_reduction,
complementary_label_filtered_image_ids,
)
feature_vector = np.concatenate(
(
label_feature_vector,
complementary_label_feature_vector,
unlabelled_image_feature_vector
)
)
ids = label_ids + complementary_label_ids + unlabelled_image_ids
_, latent_symantics = LatentSymantics(
feature_vector, k, dimension_reduction
).latent_symantics
# for i, ids in unlabelled_image_ids:
# _, latent_symantics = LatentSymantics(
# unlabelled_image_feature_vector[i], k, dimension_reduction
# ).latent_symantics
records = functions_phase2.set_records(
ids, descriptor_type, symantics_type, k, latent_symantics, pos, 5
)
for record in records:
if record["image_id"] in label_ids:
record[label] = value
elif record["image_id"] in complementary_label_ids:
record[label] = complementary_value
else:
continue
Database().insert_many(records)
def helper(feature_model, dimension_reduction, k, label_choice, image_id):
path, pos = Config().read_path(), None
descriptor_type = DescriptorType(feature_model).descriptor_type
symantics_type = LatentSymanticsType(dimension_reduction).symantics_type
label, value, complementary_value = Labels(label_choice).label
image = cv2.imread("{}{}{}".format(Config().read_all_path(), image_id,
".jpg"))
image_feature_vector = Descriptor(image, feature_model,
dimension_reduction).feature_descriptor
label_filtered_image_ids = [
item["image_id"]
for item in Database().retrieve_metadata_with_labels(label, value)
]
complementary_label_filtered_image_ids = [
item["image_id"] for item in Database().retrieve_metadata_with_labels(
label, complementary_value)
]
if DescriptorType(feature_model).check_sift():
label_feature_vector, label_ids, label_pos = functions.process_files(
path, feature_model, dimension_reduction, label_filtered_image_ids)
complementary_label_feature_vector, complementary_label_ids, complementary_label_pos = functions.process_files(
path,
feature_model,
dimension_reduction,
complementary_label_filtered_image_ids,
)
feature_vector = np.concatenate((
label_feature_vector,
complementary_label_feature_vector,
image_feature_vector,
))
pos = label_pos + complementary_label_pos + [
image_feature_vector.shape[0]
]
else:
label_feature_vector, label_ids = functions.process_files(
path, feature_model, dimension_reduction, label_filtered_image_ids)
complementary_label_feature_vector, complementary_label_ids = functions.process_files(
path,
feature_model,
dimension_reduction,
complementary_label_filtered_image_ids,
)
feature_vector = np.concatenate((
label_feature_vector,
complementary_label_feature_vector,
np.array([image_feature_vector]),
))
ids = label_ids + complementary_label_ids + [image_id]
_, latent_symantics = LatentSymantics(feature_vector, k,
dimension_reduction).latent_symantics
records = functions.set_records(ids, descriptor_type, symantics_type, k,
latent_symantics, pos, 5)
for record in records:
if record["image_id"] == image_id:
continue
elif record["image_id"] in label_ids:
record[label] = value
elif record["image_id"] in complementary_label_ids:
record[label] = complementary_value
Database().insert_many(records)
def svm_oneclass(args):
PATH = str(args.path)
DATASET = str(args.file)
DESCRIPTOR = str(args.desc)
NUM_HASH = int(args.hash)
IMG_WIDTH = int(args.width)
IMG_HEIGHT = int(args.height)
matrix_x = []
matrix_y = []
models = []
splits = []
nmatrix_x = []
nmatrix_y = []
x_train = []
y_train = []
nx_train = []
ny_train = []
plotting_labels = []
plotting_scores = []
vgg_model = None
if DESCRIPTOR == 'df':
from vggface import VGGFace
vgg_model = VGGFace()
print('>> EXPLORING DATASET')
dataset_list = load_txt_file(PATH + DATASET)
known_tuples, unknown_tuples = split_known_unknown_sets(dataset_list,
known_set_size=0.5)
known_train, known_test = split_train_test_sets(known_tuples,
train_set_size=0.5)
print(known_train)
counterA = 0
for gallery_sample in known_train:
sample_path = gallery_sample[0]
sample_name = gallery_sample[1]
gallery_path = PATH + sample_path
gallery_image = cv.imread(gallery_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
gallery_image = cv.resize(gallery_image, (IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(gallery_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(gallery_image,
vgg_model,
layer_name='fc6')
matrix_x.append(feature_vector)
matrix_y.append(sample_name)
counterA += 1
print(counterA, sample_path, sample_name)
print('>> GENERATING FILES TO SVM')
counterSVM = 0
for feature in matrix_x:
y_train.insert(counterSVM, 1)
x_train.insert(counterSVM, {})
count_inner = 0
for pos in feature:
x_train[counterSVM].update({count_inner: pos})
count_inner += 1
counterSVM += 1
print('>> GENERATING THE SVM MODEL')
x_train_total = x_train + nx_train
y_train_total = y_train + ny_train
besthit = 0
bestn = 0
bestg = 0
for n in range(1, 50):
for g in range(-15, 3):
nu = n / 100
gamma = pow(2, g)
parameters = '-s 2 -t 2'
parameters = parameters + ' -g ' + str(gamma) + ' -n ' + str(nu)
m = svm_train(y_train_total, x_train_total, parameters)
hits = 0
#print('>> LOADING KNOWN PROBE: {0} samples'.format(len(known_test)))
counterB = 0
for probe_sample in known_test:
sample_path = probe_sample[0]
sample_name = probe_sample[1]
query_path = PATH + sample_path
query_image = cv.imread(query_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
query_image = cv.resize(query_image,
(IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(query_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(
query_image, vgg_model)
count_inner = 0
x_teste = []
y_teste = []
y_teste.insert(0, 1)
x_teste.insert(0, {})
for pos in feature_vector:
x_teste[0].update({count_inner: pos})
count_inner += 1
p_label, p_acc, p_val = svm_predict(y_teste, x_teste, m)
counterB += 1
# Getting known set plotting relevant information
plotting_labels.append([(sample_name, 1)])
plotting_scores.append([(sample_name, p_label[0])])
if p_label[0] == 1:
hits = hits + 1
print('>> LOADING UNKNOWN PROBE: {0} samples'.format(
len(unknown_tuples)))
counterC = 0
for probe_sample in unknown_tuples:
sample_path = probe_sample[0]
sample_name = probe_sample[1]
query_path = PATH + sample_path
query_image = cv.imread(query_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
query_image = cv.resize(query_image,
(IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(query_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(
query_image, vgg_model)
count_inner = 0
x_teste = []
y_teste = []
y_teste.insert(0, -1)
x_teste.insert(0, {})
for pos in feature_vector:
x_teste[0].update({count_inner: pos})
count_inner += 1
p_label, p_acc, p_val = svm_predict(y_teste, x_teste, m)
counterC += 1
# Getting unknown set plotting relevant information
plotting_labels.append([(sample_name, -1)])
plotting_scores.append([(sample_name, p_label[0])])
if p_label[0] == -1:
hits = hits + 1
if hits > besthit:
besthit = hits
bestn = nu
bestg = gamma
# cmc_score_norm = np.divide(cmc_score, counterA)
# generate_cmc_curve(cmc_score_norm, DATASET + '_' + str(NUM_HASH) + '_' + DESCRIPTOR)
print(besthits)
print(bestn)
print(bestg)
pr = generate_precision_recall(plotting_labels, plotting_scores)
roc = generate_roc_curve(plotting_labels, plotting_scores)
return pr, roc
class Person:
"""A simple Person class"""
def __init__(self, img, x, y, w, h):
self.id = uuid.uuid1()
self.descriptor = Descriptor(img, x, y, w, h)
self.position = Position(x, y, w, h)
self.last_seen = time.time()
self.color = (random.randint(0, 255), random.randint(0, 255),
random.randint(0, 255))
self.is_followed = False
def add_position(self, x, y):
self.position.add_measure(x, y)
# self.position.clear_old_measures()
self.last_seen = time.time()
print("%s suivie à %s" % (self, self.last_seen))
def add_position(self, x, y, w, h):
self.position.add_measure(x + w / 2, y + h / 2)
# self.position.clear_old_measures()
self.last_seen = time.time()
formatted_now = time.ctime(int(self.last_seen))
print("%s suivie à %s" % (self, str(formatted_now)))
def add_prediction(self, x, y):
self.position.add_prediction(x, y)
# self.position.clear_old_predictions()
def get_face(self):
return self.descriptor.get_image()
def is_identified(self):
return self.descriptor.is_identified()
def set_prediction_result(self, result):
print(
"\nPersonne identifiée : {0} avec une confiance de {1}% \n".format(
result.getLabel(), result.getDist()))
self.descriptor.set_label(result.getLabel(), result.getDist())
def corresponds(self, x, y, epsilon):
return self.position.corresponds(x, y, epsilon)
def corresponds(self, x, y, w, h, epsilon):
return self.position.corresponds(x, y, w, h, epsilon)
def paint(self, image):
for i in range(len(self.position.measures) - 1):
cv2.line(image, self.position.measures[i],
self.position.measures[i + 1], self.color)
def get_last_measurement(self):
return np.array([
np.float32(self.position.measures[len(self.position.measures) -
1][0]),
np.float32(self.position.measures[len(self.position.measures) -
1][1])
])
def __str__(self):
return "Personne " + self.descriptor.name
from descriptor import Descriptor
from search import Search
import glob
import cv2
import argparse
argument_parser = argparse.ArgumentParser()
argument_parser.add_argument('-i','--index', required = True,
help = 'Path to save computed index')
argument_parser.add_argument('-q','--query', required = True,
help = 'Path to query image')
argument_parser.add_argument('-d','--dataset', required = True,
help = 'Path to result')
args = vars(argument_parser.parse_args())
color_descriptor = Descriptor((8, 12, 3))
query_image = cv2.imread(args['query'])
query_feature = color_descriptor.describe(query_image)
searcher = Search(args['index'])
feature_result = searcher.search(query_feature)
cv2.imshow('Query Image', query_image)
for score, image_name in feature_result:
result = cv2.imread(args['dataset'] + '/' + image_name)
cv2.imshow('Best Matching', result)
cv2.waitKey(0)
from os import path
import sys
print(sys.argv)
train = False
count = 1
name_video = '../sample-extractor/cutvideo.mp4'
if (len(sys.argv) >= 2):
if (sys.argv[1] == "train"):
train = True
else:
name_video = sys.argv[1]
if (not train):
print("- CARREGANDO MODELO SVM EXISTENTE -")
descriptor = Descriptor('')
svm = SVM()
svm.load("../models/trained_model.xml")
vidcap = cv2.VideoCapture(name_video)
success, image = vidcap.read()
while success:
for cut in slidingwindow.cut_frame(image):
if cut[0].shape[0] == 128 and cut[0].shape[1] == 48:
description = descriptor.describeImage(cut[0])
result = int(list(svm.test(description))[0][0])
print(result)
if (result == 1):
cv2.imwrite(
"../figs/official/positive_" + str(count) + ".jpg",
cut[0])
count += 1
def open(self):
self.descriptor = Descriptor(self)
class Detector:
"""
Class for finding objects in a video stream. Loads and utilizes a
pretrained classifier.
"""
def __init__(self,
init_size=(64, 64),
x_overlap=0.5,
y_step=0.05,
x_range=(0, 1),
y_range=(0, 1),
scale=1.5):
"""For input arguments, @see slidingwindow.#slidingWindow(...)"""
self.init_size = init_size
self.x_overlap = x_overlap
self.y_step = y_step
self.x_range = x_range
self.y_range = y_range
self.scale = scale
self.windows = None
def loadClassifier(self, filepath=None, classifier_data=None):
"""
Load a classifier trained by the functions in train.py. Either a dict
(classifier_data) or pickled file (filepath) may be supplied.
"""
if filepath is not None:
filepath = os.path.abspath(filepath)
if not os.path.isfile(filepath):
raise FileNotFoundError("File " + filepath +
" does not exist.")
classifier_data = pickle.load(open(filepath, "rb"))
else:
classifier_data = classifier_data
if classifier_data is None:
raise ValueError("Invalid classifier data supplied.")
self.classifier = classifier_data["classifier"]
self.scaler = classifier_data["scaler"]
self.cv_color_const = classifier_data["cv_color_const"]
self.channels = classifier_data["channels"]
# Simply loading the descriptor from the dict with
# self.descriptor = classifier_data["descriptor"]
# produces an error. Thus, we instantiate a new descriptor object
# using the same parameters on which the classifier was trained.
self.descriptor = Descriptor(
hog_features=classifier_data["hog_features"],
hist_features=classifier_data["hist_features"],
spatial_features=classifier_data["spatial_features"],
hog_lib=classifier_data["hog_lib"],
size=classifier_data["size"],
hog_bins=classifier_data["hog_bins"],
pix_per_cell=classifier_data["pix_per_cell"],
cells_per_block=classifier_data["cells_per_block"],
block_stride=classifier_data["block_stride"],
block_norm=classifier_data["block_norm"],
transform_sqrt=classifier_data["transform_sqrt"],
signed_gradient=classifier_data["signed_gradient"],
hist_bins=classifier_data["hist_bins"],
spatial_size=classifier_data["spatial_size"])
return self
def classify(self, image):
"""
Classify windows of an image as "positive" (containing the desired
object) or "negative". Return a list of positively classified windows.
"""
if self.cv_color_const > -1:
image = cv2.cvtColor(image, self.cv_color_const)
if len(image.shape) > 2:
image = image[:, :, self.channels]
else:
image = image[:, :, np.newaxis]
feature_vectors = [
self.descriptor.getFeatureVector(image[y_upper:y_lower,
x_upper:x_lower, :])
for (x_upper, y_upper, x_lower, y_lower) in self.windows
]
# Scale feature vectors, predict, and return predictions.
feature_vectors = self.scaler.transform(feature_vectors)
predictions = self.classifier.predict(feature_vectors)
return [
self.windows[ind] for ind in np.argwhere(predictions == 1)[:, 0]
]
def detectVideo(self,
video_capture=None,
num_frames=9,
threshold=120,
min_bbox=None,
show_video=True,
draw_heatmap=True,
draw_heatmap_size=0.2,
write=False,
write_fps=24):
"""
Find objects in each frame of a video stream by integrating bounding
boxes over several frames to produce a heatmap of pixels with high
prediction density, ignoring pixels below a threshold, and grouping
the remaining pixels into objects. Draw boxes around detected objects.
@param video_capture (VideoCapture): cv2.VideoCapture object.
@param num_frames (int): Number of frames to sum over.
@param threshold (int): Threshold for heatmap pixel values.
@param min_bbox (int, int): Minimum (width, height) of a detection
bounding box in pixels. Boxes smaller than this will not be drawn.
Defaults to 2% of image size.
@param show_video (bool): Display the video.
@param draw_heatmap (bool): Display the heatmap in an inset in the
upper left corner of the video.
@param draw_heatmap_size (float): Size of the heatmap inset as a
fraction between (0, 1) of the image size.
@param write (bool): Write the resulting video, with detection
bounding boxes and/or heatmap, to a video file.
@param write_fps (num): Frames per second for the output video.
"""
cap = video_capture
if not cap.isOpened():
raise RuntimeError("Error opening VideoCapture.")
(grabbed, frame) = cap.read()
(h, w) = frame.shape[:2]
# Store coordinates of all windows to be checked at every frame.
self.windows = slidingWindow((w, h),
init_size=self.init_size,
x_overlap=self.x_overlap,
y_step=self.y_step,
x_range=self.x_range,
y_range=self.y_range,
scale=self.scale)
if min_bbox is None:
min_bbox = (int(0.02 * w), int(0.02 * h))
# Heatmap inset size.
inset_size = (int(draw_heatmap_size * w), int(draw_heatmap_size * h))
if write:
vidFilename = datetime.now().strftime("%Y%m%d%H%M") + ".avi"
fourcc = cv2.VideoWriter_fourcc('M', 'J', 'P', 'G')
writer = cv2.VideoWriter(vidFilename, fourcc, write_fps, (w, h))
# Compute the heatmap for each frame and store in current_heatmap.
# Store the last num_frames heatmaps in deque last_N_frames. At each
# frame, sum in the deque to compute summed_heatmap. After
# thresholding, label blobs in summed_heatmap with
# scipy.ndimage.measurements.label and store in heatmap_labels.
current_heatmap = np.zeros((frame.shape[:2]), dtype=np.uint8)
summed_heatmap = np.zeros_like(current_heatmap, dtype=np.uint8)
last_N_frames = deque(maxlen=num_frames)
heatmap_labels = np.zeros_like(current_heatmap, dtype=np.int)
# Weights for the frames in last_N_frames for producing summed_heatmap.
# Recent frames are weighted more heavily than older frames.
weights = np.linspace(1 / (num_frames + 1), 1, num_frames)
frame_array = []
while True:
(grabbed, frame) = cap.read()
if not grabbed:
break
current_heatmap[:] = 0
summed_heatmap[:] = 0
for (x_upper, y_upper, x_lower, y_lower) in self.classify(frame):
current_heatmap[y_upper:y_lower, x_upper:x_lower] += 10
last_N_frames.append(current_heatmap)
for i, heatmap in enumerate(last_N_frames):
cv2.add(summed_heatmap,
(weights[i] * heatmap).astype(np.uint8),
dst=summed_heatmap)
# Apply blur and/or dilate to the heatmap.
#cv2.GaussianBlur(summed_heatmap, (5,5), 0, dst=summed_heatmap)
cv2.dilate(summed_heatmap,
np.ones((7, 7), dtype=np.uint8),
dst=summed_heatmap)
if draw_heatmap:
inset = cv2.resize(summed_heatmap,
inset_size,
interpolation=cv2.INTER_AREA)
inset = cv2.cvtColor(inset, cv2.COLOR_GRAY2BGR)
frame[:inset_size[1], :inset_size[0], :] = inset
# Ignore heatmap pixels below threshold.
summed_heatmap[summed_heatmap <= threshold] = 0
# Label remaining blobs with scipy.ndimage.measurements.label.
num_objects = label(summed_heatmap, output=heatmap_labels)
# Determine the largest bounding box around each object.
for obj in range(1, num_objects + 1):
(Y_coords, X_coords) = np.nonzero(heatmap_labels == obj)
x_upper, y_upper = min(X_coords), min(Y_coords)
x_lower, y_lower = max(X_coords), max(Y_coords)
# Only draw box if object is larger than min bbox size.
if (x_lower - x_upper > min_bbox[0]
and y_lower - y_upper > min_bbox[1]):
cv2.rectangle(frame, (x_upper, y_upper),
(x_lower, y_lower), (0, 255, 0), 6)
if write:
writer.write(frame)
if show_video:
cv2.imshow("Detection", frame)
cv2.waitKey(0) #control by myself
cap.release()
if write:
writer.release()
import sys
import grpc
import movie_pb2
import movie_pb2_grpc
import argparse
from descriptor import Descriptor
parser = argparse.ArgumentParser()
parser.add_argument("--model",help="the path of the model to save or load",\
required=True)
parser.add_argument("--address",
help="the ip and port this service want to listen",
default="[::]:5011")
parser.add_argument("--topk", help="top k", type=int, default=50)
args = parser.parse_args()
descriptor = Descriptor()
descriptor.load_model(args.model, "max")
class movieServicer(movie_pb2_grpc.FindMovieServiceServicer):
def FindMovies(self, request, context):
query = request.query
print(
time.strftime('%Y-%m-%d/%H:%M:%S', time.localtime(time.time())) +
'\t' + query)
sys.stdout.flush()
ngram_desc = descriptor.match_desc_max(query)
titles = descriptor.rank_titles(ngram_desc, args.topk)
movies = [title for title, _, _ in titles]
return movie_pb2.FindMovieReply(movies=movies)
isShuttingDown = True
input_thread = threading.Thread(target=get_user_input)
input_thread.start()
while True:
if isShuttingDown:
break
# accepts a new connection into the socket server
connectionSocket, addr = serverSocket.accept()
# ask for a nickname
nickname_message = Message(data="Digite um nickname para você: ")
connectionSocket.send(nickname_message.encode().encode('utf-8'))
# wait for nickname
nickname_payload = connectionSocket.recv(1024).decode('utf-8')
nickname_message = Message()
nickname_message.decode(nickname_payload)
nickname = nickname_message.data
# initializes the client descriptor for its thread
client = Descriptor(nickname, addr[0], addr[1], connectionSocket,
get_connected_clients, global_sender, send_to_client)
# starts the client thread
clients.append(client)
client.start()
class VehicleDetector:
def __init__(self, window_size, x_overlap, y_step, x_range, y_range,
scale):
self.window_size = window_size
self.x_overlap = x_overlap
self.y_step = y_step
self.x_range = x_range
self.y_range = y_range
self.scale = scale
self.windows = None
def load_model(self, file_path):
file_path = os.path.abspath(file_path)
if not os.path.isfile(file_path):
raise FileNotFoundError("File " + file_path + " not found.")
model_data = pickle.load(open(file_path, "rb"))
self.model = model_data["model"]
self.scaler = model_data["scaler"]
self.color_const = model_data["color_const"]
self.channels = model_data["channels"]
self.descriptor = Descriptor(
hog=model_data["hog"],
histogram=model_data["histogram"],
spatial=model_data["spatial"],
hog_size=model_data["hog_size"],
hog_bins=model_data["hog_bins"],
cell_size=model_data["cell_size"],
cells_per_block=model_data["cells_per_block"],
histogram_bins=model_data["histogram_bins"],
spatial_size=model_data["spatial_size"])
return self
def classify(self, image):
self.windows = sliding_window((image.shape[1], image.shape[0]),
window_size=self.window_size,
x_overlap=self.x_overlap,
y_step=self.y_step,
x_range=self.x_range,
y_range=self.y_range,
scale=self.scale)
if self.color_const > -1:
image = cv2.cvtColor(image, self.color_const)
feature_matrix = [
self.descriptor.get_features(image[y_upper:y_lower,
x_upper:x_lower, :])
for (x_upper, y_upper, x_lower, y_lower) in self.windows
]
feature_matrix = self.scaler.transform(feature_matrix)
predictions = self.model.predict(feature_matrix)
result_windows = []
for count, item in enumerate(predictions):
if item == 1:
result_windows.append(self.windows[count])
return result_windows
def plshface(args):
PATH = str(args.path)
DATASET = str(args.file)
DESCRIPTOR = str(args.desc)
NUM_HASH = int(args.hash)
IMG_WIDTH = int(args.width)
IMG_HEIGHT = int(args.height)
TRAIN_SET_SIZE = float(args.train_set_size)
matrix_x = []
matrix_y = []
splits = []
plotting_labels = []
plotting_scores = []
vgg_model = None
if DESCRIPTOR == 'df':
vgg_model = VGGFace()
print('>> EXPLORING DATASET')
dataset_list = load_txt_file(PATH + DATASET)
known_train, known_test = split_train_test_sets(
dataset_list, train_set_size=TRAIN_SET_SIZE)
print('>> LOADING GALLERY: {0} samples'.format(len(known_train)))
counterA = 0
for gallery_sample in known_train:
sample_path = gallery_sample[0]
sample_name = gallery_sample[1]
gallery_path = PATH + sample_path
gallery_image = cv.imread(gallery_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
gallery_image = cv.resize(gallery_image, (IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(gallery_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(gallery_image,
vgg_model,
layer_name='fc6')
matrix_x.append(feature_vector)
matrix_y.append(sample_name)
counterA += 1
print(counterA, sample_path, sample_name)
print('>> SPLITTING POSITIVE/NEGATIVE SETS')
individuals = list(set(matrix_y))
cmc_score = np.zeros(len(individuals))
for index in range(0, NUM_HASH):
splits.append(generate_pos_neg_dict(individuals))
print('>> LEARNING PLS MODELS:')
input_list = itertools.izip(splits, itertools.repeat((matrix_x, matrix_y)))
models = Parallel(n_jobs=1, verbose=11,
backend='threading')(map(delayed(learn_plsh_model),
input_list))
print('>> LOADING KNOWN PROBE: {0} samples'.format(len(known_test)))
counterB = 0
for probe_sample in known_test:
sample_path = probe_sample[0]
sample_name = probe_sample[1]
query_path = PATH + sample_path
query_image = cv.imread(query_path, cv.IMREAD_COLOR)
if DESCRIPTOR == 'hog':
query_image = cv.resize(query_image, (IMG_HEIGHT, IMG_WIDTH))
feature_vector = Descriptor.get_hog(query_image)
elif DESCRIPTOR == 'df':
feature_vector = Descriptor.get_deep_feature(
query_image, vgg_model)
vote_dict = dict(map(lambda vote: (vote, 0), individuals))
for model in models:
pos_list = [
key for key, value in model[1].iteritems() if value == 1
]
response = model[0].predict_confidence(feature_vector)
for pos in pos_list:
vote_dict[pos] += response
result = vote_dict.items()
result.sort(key=lambda tup: tup[1], reverse=True)
for outer in range(len(individuals)):
for inner in range(outer + 1):
if result[inner][0] == sample_name:
cmc_score[outer] += 1
break
counterB += 1
denominator = np.absolute(np.mean([result[1][1], result[2][1]]))
if denominator > 0:
output = result[0][1] / denominator
else:
output = result[0][1]
print(counterB, sample_name, result[0][0], output)
# Getting known set plotting relevant information
plotting_labels.append([(sample_name, 1)])
plotting_scores.append([(sample_name, output)])
cmc_score_norm = np.divide(cmc_score, counterA)
return cmc_score_norm
def process_files(positive_dir,
negative_dir,
color_space="bgr",
channels=[0, 1, 2],
hog=False,
histogram=False,
spatial=False,
hog_size=(64, 64),
hog_bins=9,
cell_size=(8, 8),
cells_per_block=(2, 2),
histogram_bins=16,
spatial_size=(16, 16)):
# take care of training files
positive_dir = os.path.abspath(positive_dir)
negative_dir = os.path.abspath(negative_dir)
if not os.path.isdir(positive_dir):
raise FileNotFoundError("Directory " + positive_dir + " not found.")
if not os.path.isdir(negative_dir):
raise FileNotFoundError("Directory " + negative_dir + " not found.")
positive_files = [
os.path.join(positive_dir, file) for file in os.listdir(positive_dir)
if os.path.isfile(os.path.join(positive_dir, file))
]
negative_files = [
os.path.join(negative_dir, file) for file in os.listdir(negative_dir)
if os.path.isfile(os.path.join(negative_dir, file))
]
print("{} positive files and {} negative files found.\n".format(
len(positive_files), len(negative_files)))
# color space info
color_space = color_space.lower()
if color_space == "hls":
color_const = cv2.COLOR_BGR2HLS
elif color_space == "hsv":
color_const = cv2.COLOR_BGR2HSV
elif color_space == "luv":
color_const = cv2.COLOR_BGR2Luv
elif color_space == "ycrcb" or color_space == "ycc":
color_const = cv2.COLOR_BGR2YCrCb
elif color_space == "yuv":
color_const = cv2.COLOR_BGR2YUV
else:
color_const = -1
# store feature vectors for both positive and negative files
positive_features = []
negative_features = []
time_begin = time.time()
# create feature descriptor object
descriptor = Descriptor(hog=hog,
histogram=histogram,
spatial=spatial,
hog_size=hog_size,
hog_bins=hog_bins,
cell_size=cell_size,
cells_per_block=cells_per_block,
histogram_bins=histogram_bins,
spatial_size=spatial_size)
# extract features from each file
for i, file_path in enumerate(positive_files + negative_files):
image = cv2.imread(file_path)
if image is None:
continue
if color_const > -1:
image = cv2.cvtColor(image, color_const)
feature_vector = descriptor.get_features(image)
if i < len(positive_files):
positive_features.append(feature_vector)
else:
negative_features.append(feature_vector)
print("Features extraction completed in {:.1f} seconds\n".format(
time.time() - time_begin))
num_features = len(positive_features[0])
# scale features
scaler = StandardScaler().fit(positive_features + negative_features)
positive_features = scaler.transform(positive_features)
negative_features = scaler.transform(negative_features)
# randomize lists of feature vectors by splitting them into training, cross-validation, and test sets
# the ratio is 75/20/5
random.shuffle(positive_features)
random.shuffle(negative_features)
num_positive_train = int(round(0.75 * len(positive_features)))
num_negative_train = int(round(0.75 * len(negative_features)))
num_positive_val = int(round(0.2 * len(positive_features)))
num_negative_val = int(round(0.2 * len(negative_features)))
positive_train = positive_features[0:num_positive_train]
negative_train = negative_features[0:num_negative_train]
positive_val = positive_features[num_positive_train:(num_positive_train +
num_positive_val)]
negative_val = negative_features[num_negative_train:(num_negative_train +
num_negative_val)]
positive_test = positive_features[(num_positive_train + num_positive_val):]
negative_test = negative_features[(num_negative_train + num_negative_val):]
print(
"Randomized images into training, cross-validation, and test sets.\n")
print("{} images in positive training set.".format(len(positive_train)))
print("{} images in positive cross-validation set.".format(
len(positive_val)))
print("{} images in positive test set.".format(len(positive_test)))
print("{} total positive images.\n".format(
len(positive_train) + len(positive_val) + len(positive_test)))
print("{} images in negative training set.".format(len(negative_train)))
print("{} images in negative cross-validation set.".format(
len(negative_val)))
print("{} images in negative test set.".format(len(negative_test)))
print("{} total negative images.\n".format(
len(negative_train) + len(negative_val) + len(negative_test)))
# store data and parameters in a dictionary
feature_data = {
"positive_train": positive_train,
"negative_train": negative_train,
"positive_val": positive_val,
"negative_val": negative_val,
"positive_test": positive_test,
"negative_test": negative_test,
"scaler": scaler,
"hog": hog,
"histogram": histogram,
"spatial": spatial,
"color_space": color_space,
"color_const": color_const,
"channels": channels,
"hog_size": hog_size,
"hog_bins": hog_bins,
"cell_size": cell_size,
"cells_per_block": cells_per_block,
"histogram_bins": histogram_bins,
"spatial_size": spatial_size,
"num_features": num_features
}
return feature_data
def insert_images_in_database(feature_model,
dimension_reduction,
k,
identifier,
set1_dir=True,
set2_dir=True):
"""
:param feature_model: 1 - CM, 2 - LBP, 3 - HOG, 4 - SIFT
:param dimension_reduction: 1 - PCA, 2 - SVD, 3 - NMF, 4 - LDA
:param k: reduced dimension value
:param identifier: 0 - Read all, 1 - Read from Labelled, 2 - Read from Unlabelled
:param set1_dir (Optional): True - Read from Set1 folder of Labelled/Unlabelled, False otherwise
:param set2_dir (Optional): True - Read from Set2 folder of Labelled/Unlabelled, False otherwise
:return None
Default case: Read from both Set1 and Set2 folders
"""
# Read images and feature extraction
if identifier == 0:
read_all_path = Config().read_all_path()
files = os.listdir(read_all_path)
connection = Database().open_connection()
db = connection[Config().database_name()]
collection = db[Config().collection_name()]
for i, file in enumerate(files):
print("Reading file: {} | {} % Done".format(
file, ((i + 1) * 100.0) / len(files)))
image = cv2.imread("{}{}".format(read_all_path, file))
feature_descriptor = Descriptor(
image, feature_model, dimension_reduction).feature_descriptor
image_id = file.replace(".jpg", "")
collection.insert_one({
"image_id": image_id,
"vector": feature_descriptor.tolist()
})
connection.close()
query_results = Database().retrieve_many()
ids = [item["image_id"] for item in query_results]
x = np.array([item["vector"] for item in query_results])
elif identifier == 1:
if set1_dir and set2_dir:
ids1, x1 = functions.process_files(
Config().read_training_set1_path(), feature_model,
dimension_reduction)
ids2, x2 = functions.process_files(
Config().read_training_set2_path(), feature_model,
dimension_reduction)
ids = ids1 + ids2
x = np.concatenate((x1, x2))
elif set1_dir:
ids, x = functions.process_files(
Config().read_training_set1_path(), feature_model,
dimension_reduction)
elif set2_dir:
ids, x = functions.process_files(
Config().read_training_set2_path(), feature_model,
dimension_reduction)
else:
if set1_dir and set2_dir:
ids1, x1 = functions.process_files(
Config().read_testing_set1_path(), feature_model,
dimension_reduction)
ids2, x2 = functions.process_files(
Config().read_testing_set2_path(), feature_model,
dimension_reduction)
ids = ids1 + ids2
x = np.concatenate((x1, x2))
elif set1_dir:
ids, x = functions.process_files(Config().read_testing_set1_path(),
feature_model,
dimension_reduction)
elif set2_dir:
ids, x = functions.process_files(Config().read_testing_set2_path(),
feature_model,
dimension_reduction)
# Find Latent_symantics
_, latent_symantics = LatentSymantics(x, k,
dimension_reduction).latent_symantics
# inserting data into Database
if identifier == 0:
records = functions.set_records(ids, latent_symantics)
Database().insert_many(records)
elif identifier == 1:
records = functions.set_records(ids, latent_symantics, training=True)
Database().insert_many(records, collection_type="training")
else:
records = functions.set_records(ids, latent_symantics)
Database().insert_many(records, collection_type="testing")
print("Done... ")