76 lines
1.6 KiB
Python
Executable file
76 lines
1.6 KiB
Python
Executable file
#!/usr/bin/env python
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import cv2, cv, sys, math, os, numpy
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from scipy.spatial import KDTree
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def extractFeatures(label):
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directory = "img/" + label + "/"
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features = []
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for fn in os.listdir(directory):
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img = cv2.imread(directory + fn, 0)
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# find edges
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canny = cv2.Canny(img, 50, 100)
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# find colored
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black_pixels = numpy.count_nonzero(img)
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# find lines lines
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lines = cv2.HoughLinesP(canny, 1, math.pi/360, 5, None, 10, 1)
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lengths = []
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angles = []
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try:
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for line in lines[0]:
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x1, y1, x2, y2 = line
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# Pythagoras
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a2 = math.pow((x1-x2), 2)
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b2 = math.pow((y1-y2), 2)
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length = int(math.sqrt(a2 + b2))
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lengths.append(length)
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angle = int(math.degrees(math.atan((y1-y2) / (x1-x2))))
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angles.append(angle)
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except:
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pass
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# print out everything
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lines_count = len(lengths)
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mid_length = sum(lengths) / lines_count
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mid_angle = sum(angles) / lines_count
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features.append([
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[lines_count, mid_length, mid_angle, black_pixels],
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label
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])
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return features
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if __name__ == "__main__":
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cats = extractFeatures("cat")
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dogs = extractFeatures("dog")
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test_count = 5
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test_data = dogs[:test_count] + cats[:test_count]
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test_labels = map(lambda a: a[1], test_data)
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test_features = map(lambda a: a[0], test_data)
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data = cats[test_count:] + dogs[test_count:]
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labels = map(lambda a: a[1], data)
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features = map(lambda a: a[0], data)
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tree = KDTree(features)
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for t in xrange(0, test_count * 2):
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d, i = tree.query(test_features[t], k=3)
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print "-"
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for j in xrange(0, len(i)):
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print test_labels[t] + " is predicted to be a " + labels[i[j]]
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