def __resize_image(self, img_original):
"""
Resize the input image
"""
print_nicer("RESIZE IMAGE - START")
start_time = time.time()
img, scale_factor = image_resize(img_original, self.resize_width,
self.resize_height, cv2.INTER_CUBIC)
print_time_info(start_time, "RESIZE IMAGE - END")
print(f"\tResized image shape: {img.shape}")
print(f"\tScale factor: {scale_factor}")
print("-" * 50)
self.show_image_main('pipeline_input', img, height=405, width=720)
return img, scale_factor
def __paintings_and_people_localization(self, paintings_detected):
"""
Execute People Localization using Information about the detected paintings.
"""
print_nicer("PEOPLE LOCALIZATION - START")
start_time = time.time()
people_room = locale_paintings_and_people(paintings_detected,
self.generator,
self.show_image,
self.print_next_step,
self.print_time)
print_time_info(start_time, "PEOPLE LOCALIZATION - END")
print("-" * 50)
return people_room
def __painting_segmentation(self, img_original, paintings_detected):
"""
Execute Painting Segmentation of the input image.
"""
print_nicer("PAINTING SEGMENTATION - START")
start_time = time.time()
painting_contours = [p.frame_contour for p in paintings_detected]
segmented_img_original = create_segmented_image(
img_original, painting_contours)
print_time_info(start_time, "PAINTING SEGMENTATION - END")
print(" Segmented shape: ", segmented_img_original.shape)
print("-" * 50)
# self.show_image_main('segmented_img_original_size', segmented_img_original, height=405, width=720)
return segmented_img_original
def __draw_painting_and_people(self, img_original, paintings_detected,
people_bounding_boxes, people_room,
scale_factor):
"""
Draw information about Paintings and People found.
"""
print_nicer("DRAW PAINTINGS AND PEOPLE INFORMATION - START")
start_time = time.time()
# First draw the info on the paintings...
draw_paintings_info(img_original, paintings_detected, scale_factor)
# ...and then the info on the people, so as to respect the prospect...
draw_people_bounding_box(img_original, people_bounding_boxes,
scale_factor)
# ...at the end the information about the room
draw_room_info(img_original, people_room, scale_factor)
print_time_info(start_time,
"DRAW PAINTINGS AND PEOPLE INFORMATION - END")
print("-" * 50)
def __painting_detection(self, img, scale_factor):
"""
Execute Painting Detection on the input image.
"""
print_nicer("PAINTING DETECTION - START")
start_time = time.time()
paintings_detected = detect_paintings(img,
self.generator,
self.show_image,
self.print_next_step,
self.print_time,
scale_factor=scale_factor)
print_time_info(start_time, "PAINTING DETECTION - END")
print("-" * 50)
self.num_paintings_detected += len(paintings_detected)
return paintings_detected
def __painting_rectification(self, img_original, paintings_detected):
"""
Execute Rectification of the painting received
"""
print_nicer("PAINTING RECTIFICATION - START")
start_time = time.time()
for i, painting in enumerate(paintings_detected):
x, y, w_rect, h_rect = painting.bounding_box
sub_img_original = img_original[y:y + h_rect, x:x + w_rect]
corners = translate_points(painting.corners, -np.array([x, y]))
painting.image = rectify_painting(sub_img_original, corners)
print_time_info(start_time, "PAINTING RECTIFICATION - END")
print("-" * 50)
if self.task == Task.painting_rectification:
for i, painting in enumerate(paintings_detected):
x, y, w_rect, h_rect = painting.bounding_box
sub_img_original = img_original[y:y + h_rect, x:x + w_rect]
# self.show_image_main(f"sub_img_original_{i}", sub_img_original)
self.show_image_main(f"painting_rectified_{i}", painting.image)
def __painting_retrieval(self, paintings_detected, scale_factor):
"""
Execute Painting Retrieval on the input image.
"""
print_nicer("PAINTING RETRIEVAL - START")
start_time = time.time()
retrieve_paintings(paintings_detected,
self.paintings_db,
self.generator,
self.show_image,
self.print_next_step,
self.print_time,
match_db_image=self.match_db_image,
histo_mode=self.histo_mode,
scale_factor=scale_factor)
print_time_info(start_time, "PAINTING RETRIEVAL - END")
print("-" * 50)
for p in paintings_detected:
if p.title is not None:
self.num_paintings_retrieved += 1
def __people_detection(self, img, paintings_detected, scale_factor):
"""
Execute People Detection on the input image.
"""
print_nicer("PEOPLE DETECTION - START")
start_time = time.time()
max_percentage = 0.85
people_bounding_boxes = detect_people(img,
self.people_detector,
paintings_detected,
self.generator,
self.show_image,
self.print_next_step,
self.print_time,
scale_factor=scale_factor,
max_percentage=max_percentage)
print_time_info(start_time, "PEOPLE DETECTION - END")
print("-" * 50)
self.num_people_detected += len(people_bounding_boxes)
return people_bounding_boxes