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"""segmentation.py: Toolkit for Generation of Instance Segmentation Material"""
# System Imports
import sys
import os
from os.path import join, exists
import json
from math import dist
# Project Imports
from loader import read_pascal_voc, read_labelme, write_labelme, load_classes_ports
from utils import transform, associated_keypoints
# Third-Party Imports
import cv2
import numpy as np
__author__ = "Amit Kumar Roy"
__copyright__ = "Copyright 2022-2023, DFKI"
__credits__ = ["Amit Kumar Roy", "Johannes Bayer"]
__license__ = "CC"
__version__ = "0.0.1"
__email__ = "johannes.bayer@dfki.de"
__status__ = "Prototype"
def binary_to_multi_seg_map(drafter: str, sample: str, suffix: str, source_folder: str, target_folder: str,
color_map: dict) -> None:
"""Creates a Multi Class Segmentation File from a Binary Segmentation File and an Coarse Instance Polygon File"""
bin_seg_map = cv2.imread(join(drafter, "segmentation", f"{sample}.{suffix}"))
bin_seg_map[np.all(bin_seg_map <= (10, 10, 10), axis=-1)] = (0, 0, 0)
shape_mask = np.ones(bin_seg_map.shape, dtype=np.uint8)*255
geo_data = read_labelme(join(drafter, source_folder, f"{sample}.json"))
for shape in sorted(geo_data["polygons"],
key=lambda shape: -(max([p[0] for p in shape['points']])-min([p[0] for p in shape['points']])) *
(max([p[1] for p in shape['points']])-min([p[1] for p in shape['points']]))):
cv2.fillPoly(shape_mask,
pts=[np.array(shape["points"], dtype=np.int32)],
color=color_map[shape["class"]])
multi_seg_map = cv2.bitwise_and(cv2.bitwise_not(bin_seg_map), shape_mask)
for point in geo_data['points']:
if point['class'] == "connector":
x, y = point['points']
cv2.line(multi_seg_map, (int(x-20), int(y-20)), (int(x+20), int(y+20)), (255, 255, 255), 2)
cv2.line(multi_seg_map, (int(x-20), int(y+20)), (int(x+20), int(y-20)), (255, 255, 255), 2)
cv2.imwrite(join(drafter, target_folder, f"{sample}.png"), multi_seg_map)
def generate_keypoints(drafter: str, sample: str, suffix: str, source_folder: str, target_folder: str,
keep_polygons: bool = True, margin=3) -> None:
"""Generates Connector Keypoints, optionally discarding existing polygons"""
bin_seg_map = cv2.imread(join(drafter, "segmentation", f"{sample}.{suffix}"), cv2.IMREAD_GRAYSCALE)
_, bin_seg_map = cv2.threshold(bin_seg_map, 127, 255, cv2.THRESH_BINARY_INV)
geo_data = read_labelme(join(drafter, source_folder, f"{sample}.json"))
detector_params = cv2.SimpleBlobDetector_Params()
detector_params.minArea = 3
detector_params.minDistBetweenBlobs = 3
detector_params.minThreshold = 10
detector_params.maxThreshold = 255
detector_params.blobColor = 255
detector_params.filterByArea = False
detector_params.filterByCircularity = False
detector_params.filterByConvexity = False
detector_params.filterByInertia = False
detector = cv2.SimpleBlobDetector_create(detector_params)
kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
for nbr, shape in enumerate(geo_data["polygons"]):
if shape['class'] == "text":
cv2.fillPoly(bin_seg_map, pts=[np.array(shape["points"], dtype=np.int32)], color=[0, 0, 0])
for nbr, shape in enumerate(geo_data["polygons"]):
shape['group'] = nbr
if shape['class'] != "text" and shape['class'] != "wire":
x_min = max(int(min([p[0] for p in shape['points']]))-margin, 0)
x_max = min(int(max([p[0] for p in shape['points']]))+margin, bin_seg_map.shape[1])
y_min = max(int(min([p[1] for p in shape['points']]))-margin, 0)
y_max = min(int(max([p[1] for p in shape['points']]))+margin, bin_seg_map.shape[0])
cropout = bin_seg_map[y_min:y_max, x_min:x_max]
shape_mask = np.zeros((y_max-y_min, x_max-x_min), dtype=np.uint8)
cv2.polylines(shape_mask, pts=[np.array(shape["points"]-np.array([[x_min, y_min]]), dtype=np.int32)],
isClosed=True, color=[255, 255, 255], thickness=2)
intersect_map = cv2.bitwise_and(cropout, shape_mask)
keypoints = detector.detect(intersect_map)
geo_data['points'] += [{'class': "connector", 'points': (keypoint.pt[0]+x_min, keypoint.pt[1]+y_min),
'group': nbr} for keypoint in keypoints]
for shape in geo_data["polygons"]:
if shape['class'] == "wire":
wire_connectors = [point["points"] for point in geo_data['points']
if cv2.pointPolygonTest(np.array(shape["points"]), np.array(point['points']), True) > -4]
if len(wire_connectors) != 2:
print(f" Anomaly Wire Connector Count: {len(wire_connectors)} -> {shape['points'][0]}")
geo_data['points'] += [{'class': "connector", 'points': (point[0], point[1]),
'group': shape['group']} for point in wire_connectors]
geo_data['polygons'] = geo_data['polygons'] if keep_polygons else []
write_labelme(geo_data, join(drafter, target_folder, f"{sample}.json"))
def generate_wires(drafter: str, sample: str, suffix: str, source_folder: str, target_folder: str) -> None:
"""Generates wire polygons"""
geo_data = read_labelme(join(drafter, source_folder, f"{sample}.json"))
bin_seg_map = cv2.imread(join(drafter, "segmentation", f"{sample}.{suffix}"), cv2.IMREAD_GRAYSCALE)
_, bin_seg_map = cv2.threshold(bin_seg_map, 127, 255, cv2.THRESH_BINARY_INV)
for nbr, shape in enumerate(geo_data["polygons"]):
cv2.fillPoly(bin_seg_map, pts=[np.array(shape["points"], dtype=np.int32)], color=[0, 0, 0])
cntrs = cv2.findContours(bin_seg_map, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
contours = cntrs[0] if len(cntrs) == 2 else cntrs[1]
for contour in contours:
if len(contour) > 3:
geo_data['polygons'] += [{'class': "wire", 'points': np.squeeze(contour).tolist(), 'group': None}]
write_labelme(geo_data, join(drafter, target_folder, f"{sample}.json"))
def pascalvoc_to_labelme(drafter: str, sample: str, suffix: str, source_folder: str, target_folder: str,
keep_existing_json: bool = True) -> None:
"""Converts a Bounding Box (Rectangle) Annotation File to an Instance Mask (Polygon) File
Has no Effect in its default Configuration on a
consistently populated Dataset."""
if keep_existing_json and exists(join(drafter, target_folder, f"{sample}.json")):
print(" -> SKIP (already exists)")
return None
xml_data = read_pascal_voc(join(drafter, source_folder, f"{sample}.xml"))
xml_data['points'] = [] # Adapt to Segmentation Scenario
xml_data['img_path'] = join("..", "segmentation", f"{sample}.{suffix}") # Alter source image
xml_data['polygons'] = [{'class': bbox['class'], 'group': None, # Keep Class, Prune Rotation and Texts
'points': [[bbox['xmin'], bbox['ymin']], # Turn Rectangles into Polygons
[bbox['xmax'], bbox['ymin']],
[bbox['xmax'], bbox['ymax']],
[bbox['xmin'], bbox['ymax']]]}
for bbox in xml_data['bboxes']]
write_labelme(xml_data, join(drafter, target_folder, f"{sample}.json"))
def labelme_raw_image(drafter: str, sample: str, suffix: str, source_folder: str, target_folder: str) -> None:
"""Resets the Source Images of a LabelME file to the Rwa Image"""
geo_data = read_labelme(join(drafter, source_folder, f"{sample}.json"))
geo_data['img_path'] = join("..", "images", f"{sample}.{suffix}")
write_labelme(geo_data, join(drafter, target_folder, f"{sample}.json"))
def convex_hull(thresh_img: np.ndarray, polygon: np.ndarray) -> list:
"""Calculates the Convex Hull of a Binary Image, falling back to Polygon"""
cntrs = cv2.findContours(thresh_img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cntrs = cntrs[0] if len(cntrs) == 2 else cntrs[1]
good_contours = [contour for contour in cntrs if cv2.contourArea(contour) > 10]
if good_contours:
contours_combined = np.vstack(good_contours)
hull = cv2.convexHull(contours_combined)
return np.squeeze(hull).tolist()
return polygon.tolist()
def refine_polygons(drafter: str, sample: str, suffix: str, source_folder: str, target_folder: str,
classes_discontinuous: list) -> None:
"""Main Function for Polygon Refinement"""
geo_data = read_labelme(join(drafter, source_folder, f"{sample}.json"))
img = cv2.imread(join(drafter, "segmentation", f"{sample}.{suffix}"))
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
(_, img) = cv2.threshold(gray, 1, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)
background_mask = np.zeros(img.shape, dtype=np.uint8)
for shape in geo_data['polygons']:
if shape["class"] != "wire":
polygon = np.array(shape["points"], dtype=np.int32)
mask_single_components = cv2.fillPoly(background_mask, pts=[polygon], color=(255, 255, 255))
bitwise_and_result = cv2.bitwise_and(img, mask_single_components)
background_mask = np.zeros(img.shape, dtype=np.uint8)
if shape["class"] in classes_discontinuous:
hull_list = convex_hull(bitwise_and_result, polygon)
shape['points'] = hull_list
else:
contours, _ = cv2.findContours(bitwise_and_result, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if contours:
contour = max(contours, key=len)
contour = np.squeeze(contour)
contour_list = contour.tolist()
shape['points'] = contour_list
else:
print(f" !!! WARNING: Empty Polygon: {shape['group']} !!!")
write_labelme(geo_data, join(drafter, target_folder, f"{sample}.json"))
def bounding_box(points):
xmin = min(point[0] for point in points)
ymin = min(point[1] for point in points)
xmax = max(point[0] for point in points)
ymax = max(point[1] for point in points)
return [xmin, ymin, xmax, ymax]
def overlap(bbox1, bbox2):
if bbox1["xmin"] > bbox2[2] or bbox1["xmax"] < bbox2[0]:
return False
if bbox1["ymin"] > bbox2[3] or bbox1["ymax"] < bbox2[1]:
return False
return True
def find_closest_points(list1, list2):
reordered_list2 = []
for x1, y1 in list1:
min_distance = float("inf")
min_point = None
for x2, y2 in list2:
distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
if distance < min_distance:
min_distance = distance
min_point = (x2, y2)
reordered_list2.append(min_point)
return [list(row) for row in reordered_list2]
def connector_type_assignment(drafter: str, sample: str, suffix: str, source_folder: str, target_folder: str) -> None:
"""Connector Point to Port Type Assignment by Geometric Transformation Matching"""
bboxes = read_pascal_voc(join(drafter, "annotations", f"{sample}.xml"))
instances = read_labelme(join(drafter, source_folder, f"{sample}.json"))
classes_ports = load_classes_ports()
bad_connector_symbols = 0
for shape in instances["polygons"]:
if shape["class"] in classes_ports.keys():
connectors = associated_keypoints(instances, shape)
cls_ports = classes_ports[shape["class"]]
bboxes_match = [bbox for bbox in bboxes['bboxes']
if overlap(bbox, bounding_box(shape["points"])) and bbox['class'] == shape['class']]
if len(cls_ports) != len(connectors):
print(f" Bad Connector Count: {shape['class']} {shape['points'][0]} -> {len(cls_ports)} vs. {len(connectors)}")
bad_connector_symbols += 1
if len(bboxes_match) != 1:
print(f" No BB for Polygon: {shape['class']} {shape['points'][0]}")
continue
if bboxes_match[0]["rotation"] is None:
print(f" Missing Rotation in BB: {shape['class']} {shape['points'][0]}")
bboxes_match[0]["rotation"] = 0
cls_ports_transformed = [transform(port, bboxes_match[0]) for port in cls_ports]
for con in connectors:
closest = sorted(cls_ports_transformed,
key=lambda cls_port: dist(cls_port['position'], con['points']))[0]
con['class'] = f"connector.{closest['name']}"
shape['rotation'] = bboxes_match[0]['rotation']
shape['text'] = bboxes_match[0]['text']
write_labelme(instances, join(drafter, target_folder, f"{sample}.json"))
return bad_connector_symbols
def pipeline(drafter: str, sample: str, suffix: str, source_folder: str, target_folder: str, **kwargs) -> None:
"""Standard Workflow"""
generate_wires(drafter, sample, suffix, source_folder, target_folder)
generate_keypoints(drafter, sample, suffix, target_folder, target_folder)
refine_polygons(drafter, sample, suffix, target_folder, target_folder, kwargs["classes_discontinuous"])
labelme_raw_image(drafter, sample, suffix, target_folder, target_folder)
return connector_type_assignment(drafter, sample, suffix, target_folder, target_folder)
def execute(function: callable, source_folder: str, target_folder: str, drafter: str, info_msg: str, **kwargs):
"""Walks through the Dataset and applies the specified Function"""
bad_connector_symbols = 0
for drafter_dir in [f"drafter_{drafter}"] if drafter else sorted(next(os.walk('.'))[1]):
if drafter_dir.startswith("drafter_"):
if not os.path.isdir(join(drafter_dir, target_folder)):
os.mkdir(join(drafter_dir, target_folder))
for sample_raw in sorted(next(os.walk(join(drafter_dir, "segmentation")))[2]):
sample, suffix = sample_raw.split(".")
print(f"{info_msg} for: {drafter_dir} -> {sample}")
res = function(drafter_dir, sample, suffix, source_folder, target_folder, **kwargs)
if res is not None:
bad_connector_symbols += res
print(f"Overall Symbols with incorrect Connector Count: {bad_connector_symbols}")
if __name__ == "__main__":
with open("classes_discontinuous.json") as f:
classes_discontinuous = json.load(f)
with open('classes_color.json') as f:
color_map = json.load(f)
commands = {"transform": [pascalvoc_to_labelme, "annotations", "instances", "Transforming BBs -> Masks", {}],
"wire": [generate_wires, "instances", "wires", "Generating Wires", {}],
"keypoint": [generate_keypoints, "instances", "keypoints", "Generating Keypoints", {}],
"create": [binary_to_multi_seg_map, "instances", "segmentation_multi_class",
"Generating Multi-Class Segmentation Map", {"color_map": color_map}],
"refine": [refine_polygons, "instances", "instances_refined", "Refining Polygons",
{"classes_discontinuous": classes_discontinuous}],
"reset": [labelme_raw_image, "instances_refined", "instances_refined",
"Resetting Source Image", {}],
"assign": [connector_type_assignment, "instances_refined", "instances_refined",
"Assigning Connector Types", {}],
"pipeline": [pipeline, "instances", "instances_refined", "Processing",
{"classes_discontinuous": classes_discontinuous}]}
if len(sys.argv) > 1 and sys.argv[1] in commands:
fun, source, target, info, paras = commands[sys.argv[1]]
drafter = sys.argv[2] if len(sys.argv) > 2 else ""
target = sys.argv[3] if len(sys.argv) > 3 else target
source = sys.argv[4] if len(sys.argv) > 4 else source
execute(fun, source, target, drafter, info, **paras)
else:
print(f"Invalid command. Must be one of: {list(commands.keys())}")
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