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Merge pull request #231 from seth10/patch-1
Update black_grayscale_line_following.py
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commit
67bed5c2c3
@ -20,9 +20,9 @@ GRAYSCALE_THRESHOLD = [(0, 64)]
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# will then be averaged with different weights where the most weight is assigned
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# to the roi near the bottom of the image and less to the next roi and so on.
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ROIS = [ # [ROI, weight]
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(0, 100, 160, 20, 0.7), # You'll need to tweak the weights for you app
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(0, 050, 160, 20, 0.3), # depending on how your robot is setup.
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(0, 000, 160, 20, 0.1)
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(0, 100, 160, 20, 0.7), # You'll need to tweak the weights for your app
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(0, 50, 160, 20, 0.3), # depending on how your robot is setup.
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(0, 0, 160, 20, 0.1)
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]
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# Compute the weight divisor (we're computing this so you don't have to make weights add to 1).
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@ -46,20 +46,15 @@ while(True):
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for r in ROIS:
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blobs = img.find_blobs(GRAYSCALE_THRESHOLD, roi=r[0:4], merge=True) # r[0:4] is roi tuple.
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if blobs:
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# Find the index of the blob with the most pixels.
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most_pixels = 0
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largest_blob = 0
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for i in range(len(blobs)):
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if blobs[i].pixels() > most_pixels:
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most_pixels = blobs[i].pixels()
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largest_blob = i
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# Find the blob with the most pixels.
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largest_blob = max(blobs, key=lambda b: b.pixels())
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# Draw a rect around the blob.
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img.draw_rectangle(blobs[largest_blob].rect())
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img.draw_cross(blobs[largest_blob].cx(),
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blobs[largest_blob].cy())
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img.draw_rectangle(largest_blob.rect())
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img.draw_cross(largest_blob.cx(),
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largest_blob.cy())
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centroid_sum += blobs[largest_blob].cx() * r[4] # r[4] is the roi weight.
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centroid_sum += largest_blob.cx() * r[4] # r[4] is the roi weight.
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center_pos = (centroid_sum / weight_sum) # Determine center of line.
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