Merge pull request spmallick#277 from krutikabapat/master

Added Hough Transform Code

Hough-Transform/README.md

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+## Hough Transform with OpenCV in C++ and Python
+
+### Hough Line Transform
+
+**Usage**
+
+**Python**
+
+- ```python3 hough_line.py road.png```
+
+**C++**
+
+- ```g++ hough_line.cpp `pkg-config opencv --cflags --libs` -o hough_line```
+- ```./hough_line```
+
+### Hough Circle Transform
+
+**Usage**
+
+**Python**
+
+- ```python3 hough_circles.py circles.png```
+
+**C++**
+
+- ```g++ hough_circles.cpp `pkg-config opencv --cflags --libs` -o hough_circles```
+- ```./hough_circles circles.png```

Hough-Transform/hough_circles.cpp

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+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/video/background_segm.hpp>
+#include <opencv2/opencv.hpp>
+#include <stdio.h>
+#include <iostream>
+#include <string>
+
+
+using namespace cv;
+using namespace std;
+
+// Declare variables to store images
+Mat dst, gray_src, cdstP, src;
+
+int thresh;
+const int thresh_max = 100;
+double t;
+
+// Vector to store circle points
+vector<Vec3f> circles;
+
+void on_trackbar( int , void* ) {
+  cdstP = gray_src.clone();
+
+  // Apply hough transform
+  HoughCircles(cdstP, circles, HOUGH_GRADIENT, 1, cdstP.rows/64, 200, 10, 1, thresh);
+
+  cdstP = src.clone();
+
+  // Draw circle on points
+  for( size_t i = 0; i < circles.size(); i++ )
+  {
+      Point center(cvRound(circles[i][0]), cvRound(circles[i][1]));
+      int radius = cvRound(circles[i][2]);
+      circle( cdstP, center, radius, Scalar(255, 255, 255), 2, 8, 0 );
+   }
+
+    cv::putText(cdstP, to_string(circles.size()), cv::Point(280, 60), cv::FONT_HERSHEY_SIMPLEX, 2, cv::Scalar(255, 0, 255), 4, cv::LINE_AA);
+    imshow( "Output-Image", cdstP);
+}
+
+
+int main(int argc, char** argv){
+  const char* file = argv[1];
+  src = imread(file, IMREAD_COLOR);
+  cv::resize(src, src, cv::Size(400, 400));
+
+  if(src.empty())
+  {
+    cout << "Error reading image" << file<< endl;
+    return -1;
+  }
+
+  // Remove noise using medianBlur
+  medianBlur(src, src, 3);
+
+  // Convert to gray-scale
+  cvtColor(src, gray_src, COLOR_BGR2GRAY);
+
+   // Will hold the results of the detection
+  namedWindow("Output-Image", 1);
+
+  thresh = 10;
+
+  createTrackbar("threshold", "Output-Image", &thresh, thresh_max, on_trackbar);
+  on_trackbar(thresh, 0);
+
+  imshow("source", src);
+  while(true)
+  {
+    int c;
+    c = waitKey( 0  );
+    if( (char)c == 27 )
+      { break; }
+  }
+
+}

Hough-Transform/hough_circles.py

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+import cv2
+import numpy as np
+import sys
+
+def onTrackbarChange(max_slider):
+    cimg = np.copy(img)
+
+    # Detect circles using HoughCircles transform
+    circles = cv2.HoughCircles(gray, cv2.HOUGH_GRADIENT, 1, cimg.shape[0]/64, param1=200, param2=10, minRadius=1, maxRadius=max_slider)
+
+    # If at least 1 circle is detected
+    if circles is not None:
+        cir_len = circles.shape[1] # store length of circles found
+        circles = np.uint16(np.around(circles))
+        for i in circles[0, :]:
+            # draw the outer circle
+            cv2.circle(cimg, (i[0], i[1]), i[2], (0, 255, 0), 2)
+            # draw the center of the circle
+            cv2.circle(cimg, (i[0], i[1]), 2, (0, 0, 255), 3)
+    else:
+        cir_len = 0 # no circles detected
+    # draw number of circles detected
+    cv2.putText(cimg, str(cir_len), (200, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (255, 0, 255), 4, cv2.LINE_AA)
+
+    cv2.imshow('Output-Image', cimg)
+
+if __name__ == "__main__":
+    # Read image
+    img = cv2.imread(sys.argv[1], 1)
+
+    if(img is None):
+        print("Image not ready properly.")
+        print("Check your file path.")
+        sys.exit(0)
+
+    cv2.namedWindow("Output-Image")
+
+    # Resize image
+    img = cv2.resize(img, (400, 400))
+
+    # Convert to gray-scale
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    # Remove noise using median blur
+    gray_blur = cv2.medianBlur(gray, 3)
+
+    thresh = 10 # initial threshold value for trackbar
+    thresh_max = 100 # maximum value for the trackbar for hough transform
+
+    cv2.createTrackbar("threshold", "Output-Image", thresh, thresh_max, onTrackbarChange)
+    onTrackbarChange(thresh)
+
+    while True:
+        cv2.imshow("source image", img)
+        key = cv2.waitKey(1)
+        if key == ord('q'):
+            break
+
+    cv2.destroyAllWindows()

Hough-Transform/hough_line.cpp

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+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/opencv.hpp>
+#include <stdio.h>
+#include <iostream>
+
+using namespace cv;
+using namespace std;
+
+// variables to store images
+Mat dst, cdstP, gray, src;
+
+int thresh;
+const int thresh_max = 100;
+double t;
+
+// create a vector to store points of line
+vector<Vec4i> linesP;
+
+void on_trackbar( int , void* )
+{ 
+  cdstP = src.clone();
+
+  // apply hough line transform
+  HoughLinesP(dst, linesP, 1, CV_PI/180, thresh, 10, 250);
+
+  // draw lines on the detected points
+   for( size_t i = 0; i < linesP.size(); i++ )
+    {
+        Vec4i l = linesP[i];
+        line( cdstP, Point(l[0], l[1]), Point(l[2], l[3]), Scalar(255,0,0), 3, LINE_AA);
+    }
+
+   // show the resultant image
+   imshow("Result Image", cdstP);
+}
+
+int main(int argc, char** argv) {
+  const char* file = argv[1];
+  // Read image (color mode)
+  src = imread(file, 1);
+
+  if(src.empty())
+  {
+    cout << "Error in reading image" << file<< endl;
+    return -1;
+  }
+
+  // Convert to gray-scale
+  cvtColor(src, gray, COLOR_BGR2GRAY);
+
+  // Detect edges using Canny Edge Detector
+  Canny(gray, dst, 50, 200, 3);
+
+  // Make a copy of original image
+  cdstP = src.clone();
+
+  // Will hold the results of the detection
+  namedWindow("Result Image", 1);
+
+  // Declare thresh to vary the max_radius of circles to be detected in hough transform
+  thresh = 50;
+
+  // Create trackbar to change threshold values
+  createTrackbar("threshold", "Result Image", &thresh, thresh_max, on_trackbar);
+  on_trackbar(thresh, 0);
+
+  // Show the final image with trackbar
+  imshow("Source Image", src);
+  while(true)
+  {
+    int c;
+    c = waitKey( 20 );
+    if( (char)c == 27 )
+      { break; }
+  }
+}

Hough-Transform/hough_line.py

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+import cv2
+import numpy as np
+import sys
+
+def onTrackbarChange(max_slider):
+	global img
+	global dst
+	global gray
+
+	dst = np.copy(img)
+	# Detect edges in the image
+	edges = cv2.Canny(gray, 50, 200)
+	# Apply hough line transform
+	lines = cv2.HoughLinesP(edges, 1, np.pi/180, max_slider, minLineLength=10, maxLineGap=250)
+
+	# Draw lines on the detected points
+	for line in lines:
+		x1, y1, x2, y2 = line[0]
+		cv2.line(dst, (x1, y1), (x2, y2), (255,0,0), 3)
+
+	cv2.imshow("Result Image", dst)	
+
+if __name__ == "__main__":
+	if(len(sys.argv) > 1):
+			# Read image
+			img = cv2.imread(sys.argv[1], 1)
+	else:
+		print("Path not specified. Please specify image path")
+		sys.exit(0)
+
+	# Create a copy for later usage
+	dst = np.copy(img)
+
+	cv2.namedWindow("Result Image")
+
+	# Convert image to gray
+	gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+	# Initialize thresh value
+	thresh = 80
+
+	# max_value of thresh
+	val = 100
+
+	cv2.createTrackbar("threshold", "Result Image", thresh, val, onTrackbarChange)
+	onTrackbarChange(val)
+
+	while True:
+		cv2.imshow("Source Image",img)
+		key = cv2.waitKey(1)
+		if key == ord('q'):
+			break
+
+	cv2.destroyAllWindows()