def diagram_to_graph_parse(diagram):
image_segment_parse = parse_image_segments(diagram)
primitive_parse = parse_primitives(image_segment_parse)
selected_primitive_parse = select_primitives(primitive_parse)
core_parse = parse_core(selected_primitive_parse)
graph_parse = parse_graph(core_parse)
return graph_parse
def test_parse_graph():
questions = geoserver_interface.download_questions(973).values()
for question in questions:
image_segment_parse = parse_image_segments(open_image(question.diagram_path))
primitive_parse = parse_primitives(image_segment_parse)
selected_primitive_parse = select_primitives(primitive_parse)
core_parse = parse_core(selected_primitive_parse)
graph_parse = parse_graph(core_parse)
print("Confident information in the diagram:")
for variable_node in parse_confident_atoms(graph_parse):
print variable_node
core_parse.display_points()
lines = get_all_instances(graph_parse, 'line')
circles = get_all_instances(graph_parse, 'circle')
arcs = get_all_instances(graph_parse, 'arc')
angles = get_all_instances(graph_parse, 'angle')
print("Displaying lines...")
for key, line in lines.iteritems():
graph_parse.display_instances([line])
print("Displaying circles...")
for key, circle in circles.iteritems():
graph_parse.display_instances([circle])
print("Displaying arcs...")
for key, arc in arcs.iteritems():
graph_parse.display_instances([arc])
print("Displaying angles...")
for key, angle in angles.iteritems():
graph_parse.display_instances([angle])
def test_parse_graph():
questions = geoserver_interface.download_questions(973).values()
for question in questions:
image_segment_parse = parse_image_segments(
open_image(question.diagram_path))
primitive_parse = parse_primitives(image_segment_parse)
selected_primitive_parse = select_primitives(primitive_parse)
core_parse = parse_core(selected_primitive_parse)
graph_parse = parse_graph(core_parse)
print("Confident information in the diagram:")
for variable_node in parse_confident_atoms(graph_parse):
print variable_node
core_parse.display_points()
lines = get_all_instances(graph_parse, 'line')
circles = get_all_instances(graph_parse, 'circle')
arcs = get_all_instances(graph_parse, 'arc')
angles = get_all_instances(graph_parse, 'angle')
print("Displaying lines...")
for key, line in lines.iteritems():
graph_parse.display_instances([line])
print("Displaying circles...")
for key, circle in circles.iteritems():
graph_parse.display_instances([circle])
print("Displaying arcs...")
for key, arc in arcs.iteritems():
graph_parse.display_instances([arc])
print("Displaying angles...")
for key, angle in angles.iteritems():
graph_parse.display_instances([angle])
def question_to_graph_parse(question):
diagram = open_image(question.diagram_path)
image_segment_parse = parse_image_segments(diagram)
primitive_parse = parse_primitives(image_segment_parse)
selected_primitive_parse = select_primitives(primitive_parse)
core_parse = parse_core(selected_primitive_parse)
graph_parse = parse_graph(core_parse)
return graph_parse
def question_to_graph_parse(question):
diagram = open_image(question.diagram_path)
image_segment_parse = parse_image_segments(diagram)
primitive_parse = parse_primitives(image_segment_parse)
selected_primitive_parse = select_primitives(primitive_parse)
core_parse = parse_core(selected_primitive_parse)
graph_parse = parse_graph(core_parse)
return graph_parse
def test_select_primitives():
question_dict = geoserver_interface.download_questions('test')
for key in sorted(question_dict.keys()):
question = question_dict[key]
print(key)
image_segment_parse = parse_image_segments(open_image(question.diagram_path))
primitive_parse = parse_primitives(image_segment_parse)
selected = select_primitives(primitive_parse)
selected.display_primitives()
def test_select_primitives():
question_dict = geoserver_interface.download_questions('test')
for key in sorted(question_dict.keys()):
question = question_dict[key]
print(key)
image_segment_parse = parse_image_segments(
open_image(question.diagram_path))
primitive_parse = parse_primitives(image_segment_parse)
selected = select_primitives(primitive_parse)
selected.display_primitives()
def save_select_primitives():
question_dict = geoserver_interface.download_questions('test')
folder_path = "/Users/minjoon/Desktop/selected/"
for key in sorted(question_dict.keys()):
question = question_dict[key]
print(key)
image_segment_parse = parse_image_segments(open_image(question.diagram_path))
primitive_parse = parse_primitives(image_segment_parse)
selected = select_primitives(primitive_parse)
image = selected.get_image_primitives()
cv2.imwrite(os.path.join(folder_path, "%s.png" % str(question.key)), image)
def save_select_primitives():
question_dict = geoserver_interface.download_questions('test')
folder_path = "/Users/minjoon/Desktop/selected/"
for key in sorted(question_dict.keys()):
question = question_dict[key]
print(key)
image_segment_parse = parse_image_segments(
open_image(question.diagram_path))
primitive_parse = parse_primitives(image_segment_parse)
selected = select_primitives(primitive_parse)
image = selected.get_image_primitives()
cv2.imwrite(os.path.join(folder_path, "%s.png" % str(question.key)),
image)
def save_parse_core():
question_dict = geoserver_interface.download_questions('test')
folder_path = "/Users/minjoon/Desktop/core/"
for key in sorted(question_dict.keys()):
print(key)
question = question_dict[key]
file_path = os.path.join(folder_path, str(question.key) + ".png")
if os.path.isfile(file_path):
continue
image_segment_parse = parse_image_segments(open_image(question.diagram_path))
primitive_parse = parse_primitives(image_segment_parse)
selected = select_primitives(primitive_parse)
core_parse = parse_core(selected)
image = core_parse.get_image_points()
cv2.imwrite(file_path, image)
def save_parse_core():
question_dict = geoserver_interface.download_questions('test')
folder_path = "/Users/minjoon/Desktop/core/"
for key in sorted(question_dict.keys()):
print(key)
question = question_dict[key]
file_path = os.path.join(folder_path, str(question.key) + ".png")
if os.path.isfile(file_path):
continue
image_segment_parse = parse_image_segments(
open_image(question.diagram_path))
primitive_parse = parse_primitives(image_segment_parse)
selected = select_primitives(primitive_parse)
core_parse = parse_core(selected)
image = core_parse.get_image_points()
cv2.imwrite(file_path, image)
def extract(image, i):
# print('----a')
image_segment_parse, detected_char = ge.parse_image_segments(image)
primitive_parse = parse_primitives(image_segment_parse)
selected = select_primitives(primitive_parse)
core_parse = parse_core(selected)
# print('----p')
graph_parse = parse_graph(core_parse)
# print('----g')
pairs = []
points = get_all_instances(graph_parse, 'point')
# print('----1')
lines = get_all_instances(graph_parse, 'line')
# print('----2')
circles = get_all_instances(graph_parse, 'circle')
# print('----3')
angles = get_all_instances(graph_parse, 'angle')
# print('----4')
polygon = get_all_instances(graph_parse, 'polygon')
# pr0int('----5')
for point in points.keys():
points[point] = (points[point].x, points[point].y)
image = core_parse.get_image_points()
for line in lines.values():
image = draw_line2(
image,
line,
offset=image_segment_parse.diagram_image_segment.offset)
# cv2.imwrite('tmp/results/im_lines.png', image)
# image = cv2.imread(image_path)
for circle in circles.values():
image = draw_circle2(
image,
circle,
offset=image_segment_parse.diagram_image_segment.offset)
cv2.imwrite('tmp/results/{}entity.png'.format(i), image)
return {
'id': i,
'points': points,
'lines': lines.keys(),
'circles': circles.keys(),
'angles': angles.keys(),
'polygon': polygon.keys(),
'chars': detected_char
}
def parse_image(
self,
image_path="C:/Users/DM/Desktop/example_1.JPG",
use_haris_centroids=True): # image_path = 'tmp/images2/0.png'
ge = Geometry_Extractor()
img = cv2.imread(image_path, 0)
## parse image
image_segment_parse, detected_char = ge.parse_image_segments(img)
# cv2.imwrite('tmp/results/source1.png', image_segment_parse.diagram_image_segment.segmented_image)
primitive_parse = parse_primitives(image_segment_parse,
parse_circles=False)
# cv2.imwrite('tmp/results/primitive1.png', primitive_parse.get_image_primitives())
selected = select_primitives(primitive_parse)
# cv2.imwrite('tmp/results/selected1.png', selected.get_image_primitives())
print("Displaying primitives...")
core_parse = parse_core(selected)
print("Displaying points...")
core_parse.display_points(display_label=False)
# cv2.imwrite('tmp/results/core1.png', core_parse.get_image_points())
# print(core_parse.intersection_points)
print(detected_char)
graph_parse = parse_graph(core_parse)
self.current_graph_parse = graph_parse
# image = cv2.imread(image_path)
# for line in lines.values():
# image = draw_line2(image, line, offset=image_segment_parse.diagram_image_segment.offset)
# cv2.imwrite('tmp/results/im_lines.png', image)
# image = cv2.imread(image_path)
# for circle in circles.values():
# image = draw_circle2(image, circle, offset=image_segment_parse.diagram_image_segment.offset)
# cv2.imwrite('tmp/results/im_circles.png', image)
match = {}
candidate_pairs = []
for char in detected_char.keys():
top_left, bottom_right = detected_char[char]
mid_point = ((top_left[0] + bottom_right[0]) / 2,
(top_left[1] + bottom_right[1]) / 2)
for pointid, loc in core_parse.intersection_points.items():
distance = (mid_point[0] - loc.x)**2 + (mid_point[1] -
loc.y)**2
candidate_pairs.append((distance, char, pointid))
candidate_pairs.sort(key=lambda x: x[0])
for pair in candidate_pairs:
if pair[0] < 500 and pair[1] not in match.keys(
) and pair[2] not in match.values():
match[pair[1]] = pair[2]
self.current_match = deepcopy(match)
print(match)
for char, idx in match.items():
match[char] = core_parse.intersection_points[idx]
self.current_match_position = match
print(match)
## harris centroids detection, refer: https://docs.opencv.org/3.4/dc/d0d/tutorial_py_features_harris.html
if use_haris_centroids:
import ipdb
ipdb.set_trace()
# find Harris corners
origin_img = core_parse.image_segment_parse.get_colored_original_image(
)
gray_img = cv2.cvtColor(origin_img, cv2.COLOR_BGR2GRAY)
gray_img = np.float32(gray_img)
# cv2.imwrite("corner.png", gray_img)
# Harris_detector = cv2.cornerHarris(gray_img, 7, 3, 0.08)
Harris_detector = cv2.cornerHarris(gray_img, 5, 3, 0.04)
cv2.imwrite("corner1.png", Harris_detector)
dst = cv2.dilate(Harris_detector, None)
cv2.imwrite("corner2.png", dst)
img_2 = cv2.imread(image_path)
# img_2[dst>0.01*dst.max()] = [255, 0, 0]
# cv2.imwrite('corner3.png', img_2)
# find centroids
ret, dst = cv2.threshold(dst, 0.01 * dst.max(), 255, 0)
dst = np.uint8(dst)
_, _, _, centroids = cv2.connectedComponentsWithStats(dst)
# Now draw centroids
for xy in centroids:
xy_int = xy.copy().astype(int)
cv2.circle(img_2, tuple(xy_int), 1, (255, 0, 0), thickness=-1)
cv2.putText(img_2,
str(xy_int),
tuple(xy_int),
cv2.FONT_HERSHEY_PLAIN,
1.0, (0, 0, 0),
thickness=1)
cv2.imwrite("image.png", img_2)
new_match = {}
new_candidate_pairs = []
for char, position in match.items():
for centroid in centroids:
distance = (centroid[0] - position.x)**2 + (centroid[1] -
position.y)**2
new_candidate_pairs.append(
(distance, char, centroid.tolist()))
new_candidate_pairs.sort(key=lambda x: x[0])
for pair in new_candidate_pairs:
if pair[1] not in new_match.keys(
) and pair[2] not in new_match.values():
new_match[pair[1]] = pair[2]
self.current_match_position = new_match
print("new_match:", new_match)
# HACK: restore format and rerank by letter-order
coordinates = []
for char, coo in self.current_match_position.items():
if use_haris_centroids:
coordinates.append("{0}({1}+7,{2}-24)".format(
char, coo[0], coo[1]))
else:
coordinates.append("{0}({1}+7,{2}-24)".format(
char, coo.x, coo.y))
coordinates.sort(key=lambda x: x[0])
return " ".join(coordinates)
os.listdir('tmp/all_images/'))): # len(os.listdir('tmp/all_images/')))
if i % 1 == 0:
print(i)
match_char = {}
try:
image = cv2.imread(image_path.format(i), cv2.IMREAD_GRAYSCALE)
# if(image.sum()) == 0:
# image = cv2.imread(image_path + image_path.format(i), -1)
# image = image[:, :, 3]
# maxd = image.max()
# mind = image.min()
# image = (1 - (image - mind) / (maxd - mind)) * 255
# image = image.astype(np.uint8)
image_segment_parse, detected_char = ge.parse_image_segments(image)
primitive_parse = parse_primitives(image_segment_parse)
selected = select_primitives(primitive_parse)
core_parse = parse_core(selected)
graph_parse = parse_graph(core_parse)
pairs = []
for char in detected_char.keys():
top_left, bottom_right = detected_char[char]
mid_point = ((top_left[0] + bottom_right[0]) / 2,
(top_left[1] + bottom_right[1]) / 2)
for pointid, loc in core_parse.intersection_points.items():
distance = (mid_point[0] - loc.x)**2 + (mid_point[1] -
loc.y)**2
pairs.append((distance, char, pointid))
for pair in pairs:
if pair[0] < 500 and pair[1] not in match_char.keys(
) and pair[2] not in match_char.values():
