Task for Vectorly

import cv2
import numpy as np

def getTextOverlay(img):
  pathname = "."
  output = np.zeros(img.shape, dtype=np.uint8)
  vis = img.copy()
  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])
    #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, 0, 0), 2)
      cv2.rectangle(vis, (rect[0],rect[1]), (rect[0]+rect[2],rect[1]+rect[3]), (255, 255, 255), 1)
  output = vis.copy()
  return output
image = cv2.imread('./simpsons_frame0.png')
print(type(image))
output = getTextOverlay(image)
cv2.imwrite('simpons_text.png', output)