Crop a pattern-based area of multiple images in python -

i have big number of screenshots need cropped. images similar - there rectangular window blue border, containing graphical elements inside. window contained inside 1 need crop inner window. across images dimensions of inner window different , content. content in cases includes elements rectangular form , - blue border, same border inner window. mentioning because thinking of following flow:

a script goes through images in target directory. each of them:

• find area cropped (inner window)
• crop area
• save file

how can done? python not compulsory, can other also.

it's not straightforward possible recipe:

import matplotlib.pyplot plt import numpy np  def synthimage():     w,h = 300,200     im = np.random.randint(0,255,(w,h,3))/255     xa = np.random.randint(50,w-60)     xb = xa + np.random.randint(50,90)     ya = np.random.randint(50,h-60)     yb = ya + np.random.randint(20,50)     im[xa:xb,ya] = np.array([1,0,0])     im[xa:xb,yb] = np.array([1,0,0])     im[xa,ya:yb] = np.array([1,0,0])     im[xb,ya:yb] = np.array([1,0,0])     return im  def getrectpoints(im):     x,y = [],[]     in range(im.shape[0]):         j in range(im.shape[1]):             if (im[i,j]-np.array([1,0,0])).sum()==0:                 x.append(i)                 y.append(j)     return np.array(x),np.array(y)  def denoise(x,y):     nx,ny = [],[]     in range(x.shape[0]):         d = np.sqrt((x[i]-x)**2+(y[i]-y)**2)         m = d<2         if len(m.nonzero()[0])>2:             nx.append(x[i])             ny.append(y[i])     return np.array(nx),np.array(ny)  im = synthimage()     plt.imshow(np.swapaxes(im,0,1),origin='lower',interpolation='nearest') plt.show()  x,y = getrectpoints(im) plt.scatter(x,y,c='red') plt.xlim(0,300) plt.ylim(0,200) plt.show()  nx,ny = denoise(x,y) plt.scatter(nx,ny,c='red') plt.xlim(0,300) plt.ylim(0,200) plt.show()  #assuming rectangle has no rotation (otherwise check scipy convehull) xmi = nx.min() xma = nx.max() ymi = ny.min() yma = ny.max()  new = np.ones(im.shape) new[xmi:xma,ymi:yma] = im[xmi:xma,ymi:yma] plt.imshow(np.swapaxes(new,0,1),origin='lower',interpolation='nearest') plt.show() 

, name of functions should self-explaining. synthetic data generated purpose of exercise. results (in order):

obviously each 1 of steps can changed depending on requirements functional solution majority of case-studies.