def get_thumbnail(cls, image_id, image_base_path):
thumbnail_path = cls._get_thumbnail_path(image_id)
if not os.path.isfile(thumbnail_path):
image = ImageDataHandler.get_image_by_id(image_id)
cls._create_resized_image(
os.path.join(image_base_path, image.file), thumbnail_path, 290)
return thumbnail_path
def put(self, label_assignment_id):
""" Update an existing label assignment """
# FIXME: This isn't called from the main thread which may result in SQL Alchemy
# session problems.
# ... but somehow it also works like this most of the time.
data = self.api.payload or {}
label_data = data.get('label')
if label_data and label_data.get('name'):
assignment = ImageDataHandler.update_label_assignment_label_name(
label_assignment_id, label_data['name']) or abort(404)
else:
assignment = ImageDataHandler.get_label_assignment_by_id(
label_assignment_id) or abort(404)
return assignment
def all_images():
return {
'images': [{
'path': f'images/{filename}',
'uid': filename,
} for filename in ImageDataHandler.filelist()]
}
def test_bounding_box(client):
ImageDataHandler.add_label_assignment('file1.jpg', 'label_with_bb',
'bb_tester', 0.9,
dict(l=0.2, r=0.3, t=0.1, b=0.5))
assignments = ImageDataHandler.get_assignments_from_origin(
'file1.jpg', 'bb_tester')
assert assignments[0].box == {
'bottom': 50.0,
'left': 20.0,
'right': 70.0,
'top': 10.0
}
assert assignments[0].confidence == 0.9
# no box
assignments = ImageDataHandler.get_assignments_from_origin(
'file1.jpg', 'nnet')
assert assignments[0].box is None
def test_filtered_images(client):
ImageDataHandler.add_label_assignment('file4.jpg', 'l_ax', 'user')
ImageDataHandler.add_label_assignment('file2.jpg', 'xl_cx', 'user')
images = ImageDataHandler.filtered_images('l_a')
assert sorted(image.file for image in images) == [
'file has spaces.jpg', 'file1.jpg', 'file2.jpg', 'file4.jpg'
]
images = ImageDataHandler.filtered_images('l*a')
assert sorted(image.file for image in images) == [
'file has spaces.jpg', 'file1.jpg', 'file2.jpg', 'file4.jpg'
]
images = ImageDataHandler.filtered_images('*c*')
assert sorted(image.file for image in images) == ['file2.jpg', 'file3.jpg']
images = ImageDataHandler.filtered_images('*')
assert sorted(image.file for image in images) == [
'file has spaces.jpg', 'file1.jpg', 'file2.jpg', 'file3.jpg',
'file4.jpg'
]
def get(self, image_id):
return ImageDataHandler.get_image_by_id(image_id)
def get(self):
all_images = ImageDataHandler.all_images()
return all_images
def get(self, label_assignment_id):
return ImageDataHandler.get_label_assignment_by_id(label_assignment_id) \
or abort(404)
def get(self, label_id):
return ImageDataHandler.get_label_by_id(label_id) or abort(404)
def get(self):
return ImageDataHandler.all_labels()
def get(self, q):
images = ImageDataHandler.filtered_images(q)
return images
def run_classifiers(config: ConfigParser):
# Load ResNet model
res_net = ResNet(config.model('resnet'), config.labels('resnet'))
res_net.load()
# Load YoloV4 model
yolo_v4 = YoloV4(config.model('yolov4'), config.labels('yolov4'))
yolo_v4.load()
for row, image_path in enumerate(tqdm(config.image_files())):
file_ending = os.path.splitext(image_path)[1].lower()
if file_ending == 'arw':
with rawpy.imread(image_path) as raw:
image = raw.post_process()
else: # if file_ending in ['jpg', 'jpeg']:
image = cv2.imread(image_path) # pylint: disable=no-member
# get relative path to image
rel_path = os.path.relpath(image_path, config.image_folder())
origin = 'ResNet:ImageNet'
if not ImageDataHandler.get_assignments_from_origin(rel_path, origin):
# classify image with resnet
labels, confidences = res_net.classify(np.copy(image))
# create new entry in database for image with relative path and top-5 labels
for (label, confidence) in zip(labels, confidences):
ImageDataHandler.add_label_assignment(rel_path, label_name=label, origin_name=origin,
confidence=confidence)
origin = 'YoloV4:COCO'
if not ImageDataHandler.get_assignments_from_origin(rel_path, origin):
# classify image with yolov4
labels, confidences, bounding_boxes = yolo_v4.classify(np.copy(image))
for (label, confidence, bounding_box) in zip(labels, confidences, bounding_boxes):
bounding_box = create_bounding_box(*[c for edge in bounding_box for c in edge])
ImageDataHandler.add_label_assignment(rel_path, label_name=label, origin_name=origin,
confidence=confidence, bounding_boxes=bounding_box)
origin = 'dlib_face_rec'
if not ImageDataHandler.get_assignments_from_origin(rel_path, origin):
scale_percent = 25 # percent of original size
width = int(image.shape[1] * scale_percent / 100)
height = int(image.shape[0] * scale_percent / 100)
dim = (width, height)
# resize image
image_resized = cv2.resize(image, dim, interpolation=cv2.INTER_AREA)
# classify image with face_recognition
bboxes = face_recognition.face_locations(image_resized, model='hog') # order: top, right, bottom, left
encodings = face_recognition.face_encodings(image_resized)
for (bbox, encoding) in zip(bboxes, encodings):
coor_orig = np.array([bbox[3], bbox[0], bbox[1], bbox[2]]) # left, top, right, bottom
coor = normalize_coordinates(coor_orig, image_resized)
bounding_box = create_bounding_box(*coor)
ImageDataHandler.add_label_assignment(rel_path, label_name='unknown_face',
origin_name=origin, bounding_boxes=bounding_box,
encoding=json.dumps(list(encoding)), editable=True)
def index():
return render_template('index.html',
images=ImageDataHandler.all_images())