import urllib import os import PIL import cv2 import numpy as np from matplotlib import pyplot as plt from pma_python import core class Annotator: def __init__(self): """ Constructor Initializes parameters and local instance variables """ # Init instance variables so to know what we have got self.pmacoreURL = "" self.pmacoreUsername = "" self.pmacorePassword = "" self.targetDir = "" self.size = 1500.0 self.client = None self.filename = "" def Start(self): """ Starts to automatic annotate a folder with the specified settings returns: None """ # connect to PMA.core core.connect(pmacoreURL=self.pmacoreURL, pmacoreUsername=self.pmacoreUsername, pmacorePassword=self.pmacorePassword) filename = self.filename # get file PMA.core info = core.get_slide_info(filename) if (info == None): print("Error in file " + filename) width = info["Width"] height = info["Height"] scale = 1.0 if(width >= height): scale = self.size / width else: scale = self.size / height parameters = {'pathOrUid': filename, 'timeframe': 0, 'layer': 0, 'x': 0, 'y': 0, 'width': width, 'height': height, 'scale': scale} if not os.path.exists(self.targetDir): os.makedirs(self.targetDir) print("Fetching file: " + filename) fname = os.path.splitext(info["Filename"].split('/')[-1])[0] + ".jpg" fname = os.path.join(self.targetDir, fname) convertToJPG = core.get_region(filename, 0, 0, width, height, scale) convertToJPG.save(fname, "JPEG") print("Contouring file") plt.axis([0, width, height, 0]) wktContours = self.Contours(fname, 1.0 / scale) print("Found " + str(len(wktContours)) + " annotations") def Contours(self, imagePath, scale): """ Tries to analyze an image and find contours returns: the Well Known Text of the calculated geometry """ img = cv2.imread(imagePath, cv2.IMREAD_COLOR) # convert to HSV color space hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) (h, s, v) = cv2.split(hsv) # blur the saturation channel s = cv2.GaussianBlur(s, (15, 15), 0) # find automatic threshold th, median = self.OtsuThreshold(s) # threshold with the automatic value ret, thresh = cv2.threshold(s, th, 255, cv2.THRESH_BINARY) # edge detect with canny # edges = cv2.Canny(thresh, 0.66 * median, 1.99 * median, # L2gradient=True) # find the contours contours0, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) # simplify and cut out small ones contours = [cv2.approxPolyDP(cnt, 3, closed=True) for cnt in contours0 if cv2.arcLength(cnt, False) > 100 or cv2.contourArea(cnt, False) > 1000] cv2.drawContours(img, contours, -1, (255, 45, 21), 3, cv2.LINE_AA) contourValues = self.ContourToWKT(contours, scale, False) # UNCOMMENT to remove all annotations before adding the new core.clear_all_annotations(self.filename, None) # using new add_annotations method from pma-python SDK (send annotation back to PMA.core) i = 0 for annotation in contourValues: i += 1 core.add_annotation(self.filename, "1", "annotation" + str(i), annotation, color="#34baeb", layerID=0) anns = core.get_annotations(self.filename) for ann in anns: surface_area = core.get_annotation_surface_area(self.filename, ann["LayerID"], ann["AnnotationID"], None) print(f"Surface Area for annotation: {ann['AnnotationID']}") print(f"\t{surface_area} mm2") return contourValues def ContourToWKT(self, contours, scale, plot=False): """ Converts openCV contours to WKT returns: the Well Known Text of the calculated geometry """ wktList = [] for index, contour in enumerate(contours): wkt = 'POLYGON((' x1 = [] y1 = [] for i, v in enumerate(contour): x1.append(v[0][0] * scale) y1.append(v[0][1] * scale) wkt += str(int(v[0][0] * scale)) + " " + str(int((v[0][1] * scale))) if i < len(contour) - 1: wkt += "," wkt += "," + str(int(x1[0])) + " " + str(int(y1[0])) if plot: plt.plot(x1, y1, '-') wkt += '))' wktList.append(wkt) if plot: plt.show() return wktList def OtsuThreshold(self, img): """ Tries to analyze an image and find a good threshold with Otsu's method returns: the threshold value, and the median value """ hist = cv2.calcHist([img], [0], None, [256], [0, 256]) sum0 = sum([t * bin for t, bin in enumerate(hist)]) median = np.median(img) # print "median = " + str(median) sumB = 0 wB = 0 wF = 0 varMax = 0 threshold = 0 total = img.size for t, bin in enumerate(hist): wB += bin # Weight Background if wB == 0: continue wF = total - wB # Weight Background if wF == 0: break sumB += t * bin mB = float(sumB / wB) mF = float((sum0 - sumB) / wF) # Calculate Between Class Variance varBetween = float(wB) * float(wF) * (mB - mF) * (mB - mF) if varBetween > varMax: varMax = varBetween threshold = t return threshold, median def usage(): print("AutoAnnotator.py [--link|-l SERVERURL] [--username|-u USERNAME] [--password|-p PASSWORD] [--filename|-f Path to slide] [--targetDir|-t tempDirectory]") print("PATH : A path to a remote root directory with images") print("SIZE : The size of the images to read") return if __name__ == '__main__': import sys import getopt try: opts, args = getopt.getopt(sys.argv[1:], "l:u:p:t:f:", ["link", "username", "password", "targetDir", "filename"]) except getopt.GetoptError as err: # print help information and exit: print(err) # will print something like "option -a not recognized" usage() sys.exit(2) annotator = Annotator() for o, a in opts: if o == "-v": verbose = True elif o in ("-l", "--link"): annotator.pmacoreURL = a elif o in ("-u", "--username"): annotator.pmacoreUsername = a elif o in ("-p", "--password"): annotator.pmacorePassword = a elif o in ("-t", "--targetDir"): annotator.targetDir = a elif o in ("-f", "--filename"): annotator.filename = a else: assert False, "unhandled option" annotator.Start() print("Done")