crop_picture.py

# make all pixels outside the polygon transparent
def get_cleaned_img(img, polygon):
  # convert to numpy (for convenience)
  imArray = np.asarray(img)

  # create mask
  maskIm = Image.new('L', (imArray.shape[1], imArray.shape[0]), 0)
  ImageDraw.Draw(maskIm).polygon(polygon, outline=1, fill=1)
  mask = np.array(maskIm)

  # assemble new image (uint8: 0-255)
  newImArray = np.empty(imArray.shape,dtype='uint8')

  # colors (three first columns, RGB)
  newImArray[:,:,:3] = imArray[:,:,:3]

  # transparency (4th column)
  newImArray[:,:,3] = mask*255

  # back to Image from numpy
  newIm = Image.fromarray(newImArray, "RGBA")
  return newIm

def crop_picture(img, polygon):
  img = np.asarray(img)
  # vertices of the cropping polygon
  xc = np.array([c[0] for c in polygon])
  yc = np.array([c[1] for c in polygon])
  xycrop = np.vstack((xc, yc)).T

  # xy coordinates for each pixel in the image
  nr, nc, channels = img.shape
  ygrid, xgrid = np.mgrid[:nr, :nc]
  xypix = np.vstack((xgrid.ravel(), ygrid.ravel())).T

  # construct a Path from the vertices
  pth = Path(xycrop, closed=False)

  # test which pixels fall within the path
  mask = pth.contains_points(xypix)

  # reshape to the same size as the image
  mask = mask.reshape((nr, nc))
  mask = img * mask[:, :, None]

  # create a masked array
  masked = np.ma.masked_array(img, ~mask)

  # if you want to get rid of the blank space above and below the cropped
  # region, use the min and max x, y values of the cropping polygon:

  xmin, xmax = int(xc.min()), int(np.ceil(xc.max()))
  ymin, ymax = int(yc.min()), int(np.ceil(yc.max()))
  trimmed = masked[ymin:ymax, xmin:xmax]

  newIm = Image.fromarray(trimmed, "RGBA")
  return newIm