How can I detect a 'dark' image border and crop to it using Python (or Perl)?

Question

I have a number of small images that are screen captures from a video. A couple of example images follow (with various typesof edges present):

In short, I'm trying to crop the image to the closest part of the 'main' image, which is inside an (almost) uniformly 'black' border... or sometimes, there's a bit of a 'jittery' edge. You could think of it as going to the centre of the image and then radiate out until you hit a 'rectangular ('black' or 'nearly black') border'.

The biggest issue as near as I can see is to determine the location and dimensions of the 'cropping rectangle' around the image.. but so far, I haven't been able to get anywhere with doing that.

I've tried using 'cropdetect' filters in ffmpeg; there's not anything really helpful with Perl; ...and as I'm new to Python, I still haven't worked-out if there's any simple module that can do what I need. I have looked at 'scikit-image'... but was totally bamboozled by it, as I don't have a good enough knowledge of Python, let alone a sufficientlly 'technical' knowledge of image formats, colour depth, manipulation techniques, etc that would let me use 'scikit-image'.

I'd appreciate any suggestions on how to tackle this problem, even better if there was a simple way to do it. From my little bit of understanding of 'scikit-image', it seems like the 'Canny edge detect' or the 'prewitt_v'/'prewitt_h' filters might be relevant...?

I'm using Python 3.7.0 (and Active State Perl v5.20.2, if there's any way to use that), both running under Windows 8.1.

Thanks a lot for any forthcoming suggestions.

Answer 1

I've made an attempt at solving this... but it's not very 'robust'. It uses the luminance values of the pixels when the image has been greyscaled:-

# ----
#  this will find the 'black'-ish portions and crop the image to these points
#  ozboomer, 25-Apr-2020 3-May-2020
#
# ---------

import pprint
import colorsys

from PIL import Image, ImageFilter

# ---- Define local functions (I *still* don't understand why I have to put these here)

def calculate_luminances(r, g, b):
   """Return luminance values of supplied RGB and greyscale of RGB"""

   lum = (0.2126 * r) + (0.7152 * g) + (0.0722 * b)    # luminance

   H, S, V = colorsys.rgb_to_hsv(r, g, b)              # HSV for the pixel RGB
   R, G, B = colorsys.hsv_to_rgb(H, 0, V)              # ...and greyscale RGB

   glum = (0.2126 * R) + (0.7152 * G) + (0.0722 * B)   # greyscale luminance

   return(lum, glum)

# end calculate_luminances

def radial_edge(radial_vector, ok_range):
   """Return the point in the radial where the luminance marks an 'edge'  """

   print("radial_edge: test range=", ok_range)

   edge_idx = -1
   i = 0

   for glum_value in radial_vector:
      print("  radial_vector: i=", i, "glum_value=", "%.2f" % round(glum_value, 2))

      if int(glum_value) in ok_range:
         print("  IN RANGE!  Return i=", i)
         edge_idx = i
         break

      i = i + 1      
   # endfor         

   return(edge_idx)

# ---- End local function definitions

# ---- Define some constants, variables, etc

#image_file = "cap.bmp"
#image_file = "cap2.png"
#image_file = "cap3.png"
image_file = "Sample.jpg"
#image_file = "cap4.jpg"
output_file = "Cropped.png";

edge_threshold = range(0, 70)  # luminance in this range = 'an edge'

#
# The image layout:-
#
# [0,0]----------+----------[W,0]
#   |            ^            |
#   |            |            |
#   |            R3           |
#   |            |            |
#   +<--- R1 ---[C]--- R2 --->+
#   |            |            |
#   |            R4           |
#   |            |            |
#   |            v            |
# [0,H]----------+----------[W,H]
#

# -------------------------------------
#  Main Routine
#

# ---- Get the image file ready for processing

try:
   im = Image.open(image_file)  # RGB.. mode

except:
   print("Unable to load image,", image_file)
   exit(1)

# Dammit, Perl, etc code is SO much less verbose:-
# open($fh, "<", $filename) || die("\nERROR: Can't open file, '$filename'\n$!\n");

print("Image - Format, size, mode: ", im.format, im.size, im.mode)

W, H = im.size         # The (width x height) of the image
XC = int(W / 2.0)      # Approx. centre of image
YC = int(H / 2.0)

print("Image Centre: (XC,YC)=", XC, ",", YC)

# --- Define the ordinate ranges for each radial

R1_range = range(XC, -1, -1)  # Actual range: XC->0 by -1 ... along YC ordinate
R2_range = range(XC,  W,  1)  #             : XC->W by +1 ... along YC ordinate
R3_range = range(YC, -1, -1)  #             : YC->0 by -1 ... along XC ordinate
R4_range = range(YC,  H,  1)  #             : YC->H by +1 ... along XC ordinate 

# ---- Check each radial for its 'edge' point

radial_luminance = []
for radial_num in range (1,5):  # We'll do the 4 midlines
   radial_luminance.clear()

   if radial_num == 1:
      print("Radial: R1")
      for x in R1_range:
         R, G, B = im.getpixel((x, YC))
         [lum, glum] = calculate_luminances(R, G, B)
         print("  CoOrd=(", x, ",", YC, ") RGB=", 
               (R, G, B), "lum=", "%.2f" % round(lum, 2), 
               "glum=", "%.2f" % round(glum, 2))         
         radial_luminance.append(glum)
      # end: get another radial pixel         

      left_margin = XC - radial_edge(radial_luminance, edge_threshold)

   elif radial_num == 2: 
      print("Radial: R2")
      for x in R2_range:
         R, G, B = im.getpixel((x, YC))
         [lum, glum] = calculate_luminances(R, G, B)
         print("  CoOrd=(", x, ",", YC, ") RGB=", 
               (R, G, B), "lum=", "%.2f" % round(lum, 2), 
               "glum=", "%.2f" % round(glum, 2))         
         radial_luminance.append(glum)  
      # end: get another radial pixel         

      right_margin = XC + radial_edge(radial_luminance, edge_threshold)

   elif radial_num == 3: 
      print("Radial: R3")
      for y in R3_range:
         R, G, B = im.getpixel((XC, y))
         [lum, glum] = calculate_luminances(R, G, B)
         print("  CoOrd=(", XC, ",", y, ") RGB=", 
               (R, G, B), "lum=", "%.2f" % round(lum, 2), 
               "glum=", "%.2f" % round(glum, 2))         
         radial_luminance.append(glum)  
      # end: get another radial pixel         

      top_margin = YC - radial_edge(radial_luminance, edge_threshold)

   elif radial_num == 4: 
      print("Radial: R4")
      for y in R4_range:
         R, G, B = im.getpixel((XC, y))
         [lum, glum] = calculate_luminances(R, G, B)
         print("  CoOrd=(", XC, ",", y, ") RGB=", 
               (R, G, B), "lum=", "%.2f" % round(lum, 2), 
               "glum=", "%.2f" % round(glum, 2))         
         radial_luminance.append(glum)  
      # end: get another radial pixel         

      bottom_margin = YC + radial_edge(radial_luminance, edge_threshold)

   # end: which radial we're processing      

im.close()
crop_items = (left_margin, top_margin, right_margin, bottom_margin)
print("crop_items:", crop_items)

# ---- Crop the original image and save it

im = Image.open(image_file)
im2 = im.crop(crop_items)      
im2.save(output_file, 'png')

exit(0)

# [eof]

I would expect the radial_edge() function would need to be modified to do something about checking the surrounding pixels to determine if we have a real edge... 'coz the current ok_range probably needs to be determined for each image, so there's no point to trying to automate the cropping using a script such as this.

Still looking for a robust and reliable way to attack this problem...