edited hough transform tutorial

x4nth055 · x4nth055 · commit 43dfad1b379c · 2020-04-16T17:36:35.000+02:00
diff --git a/machine-learning/shape-detection/README.md b/machine-learning/shape-detection/README.md
@@ -8,4 +8,8 @@ To run this:
 - To detect lines of live camera:
     ```
     python live_shape_detector.py
+    ```
+- To detect circles on `coins.jpg` image, consider:
+    ```
+    python circle_detector.py coins.jpg
     ```
diff --git a/machine-learning/shape-detection/circle_detector.py b/machine-learning/shape-detection/circle_detector.py
@@ -0,0 +1,32 @@
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import sys
+
+# load the image
+img = cv2.imread(sys.argv[1])
+# convert BGR to RGB to be suitable for showing using matplotlib library
+img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+# make a copy of the original image
+cimg = img.copy()
+# convert image to grayscale
+img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+# apply a blur using the median filter
+img = cv2.medianBlur(img, 5)
+# finds the circles in the grayscale image using the Hough transform
+circles = cv2.HoughCircles(image=img, method=cv2.HOUGH_GRADIENT, dp=0.9, 
+                            minDist=80, param1=110, param2=39, maxRadius=70)
+
+for co, i in enumerate(circles[0, :], start=1):
+    # draw the outer circle in green
+    cv2.circle(cimg,(i[0],i[1]),i[2],(0,255,0),2)
+    # draw the center of the circle in red
+    cv2.circle(cimg,(i[0],i[1]),2,(0,0,255),3)
+    
+# print the number of circles detected
+print("Number of circles detected:", co)
+# save the image, convert to BGR to save with proper colors
+# cv2.imwrite("coins_circles_detected.png", cimg)
+# show the image
+plt.imshow(cimg)
+plt.show()
diff --git a/machine-learning/shape-detection/coins.jpg b/machine-learning/shape-detection/coins.jpg
