If it's 1=grayscale, you know the answer already without needing to inspect individual pixels. (Also my original remark stands, first you check the JPEG header, offset 6: number of components (1 = grayscale, 3 = RGB). Return (any(isColor(*pixel)) for pixel in im.iterpixels()) This is because Windows allows you to set DPI scales at 125. While mostly integral on MacOS (unless originating from some code that’s probably not ours), it can vary on Windows, and perhaps arguably mostly fractional. #for pixel in im.getpixel(.): # you could traverse rows linearly, or modulo (say) (im.height * 2./3) -1 For the purposes of Retina/HiDPI, the scale factor is used when converting user space coordinates to backing store coordinates. # Now traverse entire image (yes we will unwantedly revisit the nrand points once) Typical size of contaminants and particles are indicated below. # First, generate a few random pixels from entire image Here you will find over 100 articles and books for the Xojo (formerly REALbasic/REAL. Optimize PDF files (optimize content streams, scale images, re-compress images. ed(randseed) # For deterministic behavior in test Add actions to annotations like JavaScript, GoToR and GoTo. I don't have PIL installed so I can't test this, here's the outline: import Imageĭef isColor(r,g,b): # use tuple-unpacking to unpack pixel -> r,g,bĭef iterpixels(nrand=100, randseed=None): This result falls beyond the top 1M of websites and. First we sample(-without-replacement) say 100 random i,j-points in the unlikely event that isn't conclusive, then we scan it linearly. (including web cam support), extensive BMP image manipulation, matrix. In fact, the total size of main page is 455.6 kB. A performance-enhance for fast results: since many images have black or white border, you'd expect faster termination by sampling a few random i,j-points from im and test them? Or use modulo arithmetic to traverse the image rows.
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