Source code for dtlpy.entities.annotation_definitions.polygon

import numpy as np
import logging

from . import BaseAnnotationDefinition

logger = logging.getLogger(name='dtlpy')

[docs]class Polygon(BaseAnnotationDefinition): """ Polygon annotation object """ type = "segment" def __init__(self, geo, label, attributes=None, description=None): super().__init__(description=description, attributes=attributes) self.geo = geo self.label = label @property def x(self): return self.geo[:, 0] @property def y(self): return self.geo[:, 1] @property def left(self): return np.min(self.x) @property def top(self): return np.min(self.y) @property def right(self): return np.max(self.x) @property def bottom(self): return np.max(self.y) def to_coordinates(self, color): return [[{"x": float(x), "y": float(y)} for x, y in self.geo]] @staticmethod def from_coordinates(coordinates): return np.asarray([[pt["x"], pt["y"]] for pt in coordinates])
[docs] def show(self, image, thickness, with_text, height, width, annotation_format, color, alpha=1): """ Show annotation as ndarray :param image: empty or image to draw on :param thickness: :param with_text: not required :param height: item height :param width: item width :param annotation_format: options: list(dl.ViewAnnotationOptions) :param color: color :param alpha: opacity value [0 1], default 1 :return: ndarray """ try: import cv2 except (ImportError, ModuleNotFoundError): self.logger.error( 'Import Error! Cant import cv2. Annotations operations will be limited. import manually and fix errors') raise if thickness is None: thickness = 2 # create image to draw on if alpha != 1: overlay = image.copy() else: overlay = image overlay = cv2.drawContours( image=overlay, contours=[np.round(self.geo).astype(int)], contourIdx=-1, color=color, thickness=thickness, ) if not isinstance(color, int) and len(color) == 4 and color[3] != 255: # add with opacity image = cv2.addWeighted(src1=overlay, alpha=alpha, src2=image, beta=1 - alpha, gamma=0) else: image = overlay if with_text: image = self.add_text_to_image(image=image, annotation=self) return image
[docs] @classmethod def from_segmentation(cls, mask, label, attributes=None, epsilon=None, max_instances=1, min_area=0): """ Convert binary mask to Polygon :param mask: binary mask (0,1) :param label: annotation label :param attributes: annotations list of attributes :param epsilon: from opencv: specifying the approximation accuracy. This is the maximum distance between the original curve and its approximation. if 0 all points are returns :param max_instances: number of max instances to return. if None all wil be returned :param min_area: remove polygons with area lower thn this threshold (pixels) :return: Polygon annotation """ try: import cv2 except (ImportError, ModuleNotFoundError): raise ImportError('opencv not found. Must install to perform this function') # mask float mask = 1. * mask # normalize to 1 mask /= np.max(mask) # threshold the mask ret, thresh = cv2.threshold(mask, 0.5, 255, 0) # find contours major, minor, _ = cv2.__version__.split(".") if int(major) > 3: contours, hierarchy = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) else: _, contours, hierarchy = cv2.findContours(thresh.astype(np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE) if len(contours) == 0: # no contours were found new_pts_list = [] else: # calculate contours area areas = np.asarray([cv2.contourArea(cnt) for cnt in contours]) # take onr contour with maximum area sorted_areas_inds = areas.argsort()[::-1] filtered_contours = [contours[s_ind] for s_ind in sorted_areas_inds if areas[s_ind] > min_area] # filter by area size filtered_contours = filtered_contours[:max_instances] # take only the first max_instance of the results # estimate contour to reduce number of points new_pts_list = list() for curve in filtered_contours: if epsilon is None: epsilon = 0.0005 * cv2.arcLength(curve=curve, closed=True) estimated_polygon = np.squeeze(cv2.approxPolyDP(curve=curve, epsilon=epsilon, closed=True)) if len(estimated_polygon.shape) == 1: new_pts_list.append(np.squeeze(curve)) else: new_pts_list.append(estimated_polygon) polygons = [cls(geo=new_pts, label=label, attributes=attributes, ) for new_pts in new_pts_list] if len(polygons) == 1: polygons = polygons[0] return polygons
@classmethod def from_json(cls, _json): if "coordinates" in _json: geo = cls.from_coordinates(coordinates=_json["coordinates"][0]) elif "data" in _json: geo = cls.from_coordinates(coordinates=_json["data"][0]) else: raise ValueError( 'can not find "coordinates" or "data" in annotation. id: %s' % _json["id"] ) return cls( geo=geo, label=_json["label"], attributes=_json.get("attributes", None), )