Detect resistors using contours

3/contours.py

@@ -0,0 +1,69 @@
+# -*- coding: utf-8 -*-
+# vim:fenc=utf-8
+#
+# Copyright © 2018 pavle <pavle.portic@tilda.center>
+#
+# Distributed under terms of the MIT license.
+
+import cv2 as cv
+import numpy as np
+
+def main():
+	#  for i in [3, 7]:
+	for i in range(1, 11):
+		filename = 'set/A' + str(i) + '.png'
+		src = cv.imread(filename)
+
+		w, h, c = src.shape
+		resize_coeff = 0.4
+		src = cv.resize(src, (int(resize_coeff * h), int(resize_coeff * w)))
+		box = find_resistor(src)
+		cv.drawContours(src, [box], 0, (0, 255, 0), 1)
+		cv.imshow('img', src)
+
+		box = sorted(box, key=lambda coord: coord[0])
+
+		x = abs(box[0][0] - box[2][0])
+		y = abs(box[0][1] - box[2][1])
+		phi = np.arctan2(y, x)
+
+		print box
+
+		if box[0][1] < box[2][1]:
+			print 'right', np.degrees(phi)
+		elif box[0][1] > box[2][1]:
+			print 'left', np.degrees(phi)
+		else:
+			print 'hor', np.degrees(phi)
+
+		cv.waitKey()
+
+
+def find_resistor(img):
+	img = contrast(img, 1.5)
+	img = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
+	img = cv.medianBlur(img, 7)
+	img = cv.Canny(img, 100, 150)
+	kernel = np.ones((9, 9), np.uint8)
+	img = cv.dilate(img, kernel, 2)
+	img = cv.erode(img, kernel, 2)
+	img, contours, hierarchy = cv.findContours(img, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
+
+	if len(contours) is not 0:
+		max_index, max_area = max(enumerate([cv.contourArea(x) for x in contours]), key = lambda x: x[1])
+		max_contour = contours[max_index]
+		rect = cv.minAreaRect(max_contour)
+		return np.int0(cv.boxPoints(rect))
+
+
+def contrast(img, clipLimit):
+	img = cv.cvtColor(img, cv.COLOR_BGR2LAB)
+	l, a, b = cv.split(img)
+	clahe = cv.createCLAHE(clipLimit, tileGridSize=(8,8))
+	cl = clahe.apply(l)
+	img = cv.merge((cl, a, b))
+	return cv.cvtColor(img, cv.COLOR_LAB2BGR)
+
+
+if __name__ == "__main__":
+	main()

3/hough.py

@@ -6,49 +6,46 @@
 #
 # Distributed under terms of the MIT license.
 
-"""
-@file hough_lines.py
-@brief This program demonstrates line finding with the Hough transform
-"""
-import sys
-import math
 import cv2 as cv
 import numpy as np
-def main(argv):
-	default_file =  "set/A6.png"
-	filename = argv[0] if len(argv) > 0 else default_file
-	# Loads an image
-	src = cv.imread(filename, cv.IMREAD_GRAYSCALE)
-	# Check if image is loaded fine
-	if src is None:
-		print ('Error opening image!')
-		print ('Usage: hough_lines.py [image_name -- default ' + default_file + '] \n')
-		return -1
 
-	dst = cv.Canny(src, 50, 200, None, 3)
+def main():
+	filename = "set/A1.png"
+	src = cv.imread(filename)
+	#  dst = cv.Canny(src, 50, 200, None, 3)
 
-	# Copy edges to the images that will display the results in BGR
-	cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
-	lines = cv.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
+	edges = cv.Canny(src, 20, 250, apertureSize = 3)
+	lines = cv.HoughLinesP(edges, 1, np.pi/180, 100, minLineLength=100, maxLineGap=10)
+	dest = cv.cvtColor(edges, cv.COLOR_GRAY2BGR)
 
-	if lines is not None:
-		#  for i in range(0, len(lines)):
-		for i in [ 0 ]:
-			rho = lines[i][0][0]
-			theta = lines[i][0][1]
-			a = math.cos(theta)
-			b = math.sin(theta)
-			x0 = a * rho
-			y0 = b * rho
-			pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
-			pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))
-			cv.line(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)
-	print type(lines)
+	for line in lines:
+		x1,y1,x2,y2 = line[0]
+		#  cv.line(dest, (x1, y1), (x2, y2), (0, 0, 255), 2)
 
-	cv.imshow("Source", src)
-	cv.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst)
+	cv.imshow('Detected', dest)
 	cv.waitKey()
+
+	#  cdst = cv.cvtColor(dst, cv.COLOR_GRAY2BGR)
+	#  lines = cv.HoughLines(dst, 1, np.pi / 180, 150, None, 0, 0)
+
+	#  if lines is not None:
+		#  for i in [ 0 ]:
+			#  rho = lines[i][0][0]
+			#  theta = lines[i][0][1]
+			#  a = np.cos(theta)
+			#  b = np.sin(theta)
+			#  x0 = a * rho
+			#  y0 = b * rho
+			#  pt1 = (int(x0 + 1000*(-b)), int(y0 + 1000*(a)))
+			#  pt2 = (int(x0 - 1000*(-b)), int(y0 - 1000*(a)))
+			#  cv.line(cdst, pt1, pt2, (0,0,255), 3, cv.LINE_AA)
+			#  print type(lines)
+
+	#  cv.imshow("Source", src)
+	#  cv.imshow("Detected Lines (in red) - Standard Hough Line Transform", cdst)
+	#  cv.waitKey()
 	return 0
 
+
 if __name__ == "__main__":
-	main(sys.argv[1:])
+	main()