Restructure directories to prepare for TensorFlow

ml/classify.py

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