erGrouping python bindings and sample script textdetection.py which mimics the same detection pipeline as in textdetection.cpp

modules/text/samples/textdetection.py

@@ -0,0 +1,59 @@
+#!/usr/bin/python
+
+import sys
+import os
+
+import cv2
+import numpy as np
+from matplotlib import pyplot as plt
+
+print('\ntextdetection.py')
+print('       A demo script of the Extremal Region Filter algorithm described in:')
+print('       Neumann L., Matas J.: Real-Time Scene Text Localization and Recognition, CVPR 2012\n')
+
+
+if (len(sys.argv) < 2):
+  print(' (ERROR) You must call this script with an argument (path_to_image_to_be_processed)\n')
+  quit()
+
+pathname = os.path.dirname(sys.argv[0])
+
+
+img      = cv2.imread(str(sys.argv[1]))
+vis      = img.copy() # for visualization 
+
+
+# Extract channels to be processed individually
+channels = cv2.text.computeNMChannels(img)
+# Append negative channels to detect ER- (bright regions over dark background)
+cn = len(channels)-1
+for c in range(0,cn):
+  channels.append((255-channels[c]))
+
+# Apply the default cascade classifier to each independent channel (could be done in parallel)
+print("Extracting Class Specific Extremal Regions from "+str(len(channels))+" channels ...")
+print("    (...) this may take a while (...)")
+for channel in channels:
+
+  erc1 = cv2.text.loadClassifierNM1(pathname+'/trained_classifierNM1.xml')
+  er1 = cv2.text.createERFilterNM1(erc1,16,0.00015,0.13,0.2,True,0.1)
+
+  erc2 = cv2.text.loadClassifierNM2(pathname+'/trained_classifierNM2.xml')
+  er2 = cv2.text.createERFilterNM2(erc2,0.5)
+
+  regions = cv2.text.detectRegions(channel,er1,er2)
+
+  rects = cv2.text.erGrouping(img,channel,[r.tolist() for r in regions])
+  #rects = cv2.text.erGrouping(img,gray,[x.tolist() for x in regions], cv2.text.ERGROUPING_ORIENTATION_ANY,'../../GSoC2014/opencv_contrib/modules/text/samples/trained_classifier_erGrouping.xml',0.5)
+
+  #Visualization
+  for r in range(0,np.shape(rects)[0]):
+    rect = rects[r]
+    cv2.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (0, 255, 255), 2)
+
+
+#Visualization
+vis = vis[:,:,::-1] #flip the colors dimension from BGR to RGB
+plt.imshow(vis)
+plt.xticks([]), plt.yticks([])  # to hide tick values on X and Y axis
+plt.show()