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Revealjs presentation available in GitPitch
Deskewing simple grayscale images can be achieved using image moments (distance and intensity of pixels).
def deskew(img): m = cv2.moments(img) if abs(m['mu02']) < 1e-2: # no deskewing needed. return img.copy() # Calculate skew based on central momemts. skew = m['mu11']/m['mu02'] # Calculate affine transform to correct skewness. M = np.float32([[1, skew, -0.5*SZ*skew], [0, 1, 0]]) # Apply affine transform img = cv2.warpAffine(img, M, (SZ, SZ), flags=cv2.WARP_INVERSE_MAP | cv2.INTER_LINEAR) return img
This deskewing of simple grayscale images can be achieved using image moments. OpenCV has an implementation of moments and it comes in handy while calculating useful information like centroid, area, skewness of simple images with black backgrounds.
It turns out that a measure of the skewness is the given by the ratio of the two central moments ( mu11 / mu02 ). The skewness thus calculated can be used in calculating an affine transform that deskews the image.
Increase image contrast using the image’s histogram.
We took a kaggle competition as a trial project to help us acquire an experience in real world data issues without too much hassle on cleaning and getting the data. The objective of this competition is to contribute to fisheries monitoring by finding the best algorithm classifying into seven species pictures caught from fishing boats.
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