Added contour detection

Author: mehabhalodiya

Contour-Detection/live_contour_det.py

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+# importing libraries
+import cv2
+import numpy as np
+
+# initialize camera settings
+frameWidth = 640
+frameHeight = 480
+cap = cv2.VideoCapture(0)
+cap.set(3, frameWidth)
+cap.set(4, frameHeight)
+
+def empty(a):
+    pass
+
+# name of the window
+cv2.namedWindow("Parameters")
+
+# resize the window
+cv2.resizeWindow("Parameters",640,240)
+
+#creating trackbars
+cv2.createTrackbar("Threshold1","Parameters",23,255,empty)
+cv2.createTrackbar("Threshold2","Parameters",20,255,empty)
+cv2.createTrackbar("Area","Parameters",5000,30000,empty)
+
+# function for stacking images together
+def stackImages(scale,imgArray):
+    rows = len(imgArray)
+    cols = len(imgArray[0])
+    rowsAvailable = isinstance(imgArray[0], list)
+    width = imgArray[0][0].shape[1]
+    height = imgArray[0][0].shape[0]
+    if rowsAvailable:
+        for x in range ( 0, rows):
+            for y in range(0, cols):
+                if imgArray[x][y].shape[:2] == imgArray[0][0].shape [:2]:
+                    imgArray[x][y] = cv2.resize(imgArray[x][y], (0, 0), None, scale, scale)
+                else:
+                    imgArray[x][y] = cv2.resize(imgArray[x][y], (imgArray[0][0].shape[1], imgArray[0][0].shape[0]), None, scale, scale)
+                if len(imgArray[x][y].shape) == 2: imgArray[x][y]= cv2.cvtColor( imgArray[x][y], cv2.COLOR_GRAY2BGR)
+        imageBlank = np.zeros((height, width, 3), np.uint8)
+        hor = [imageBlank]*rows
+        hor_con = [imageBlank]*rows
+        for x in range(0, rows):
+            hor[x] = np.hstack(imgArray[x])
+        ver = np.vstack(hor)
+    else:
+        for x in range(0, rows):
+            if imgArray[x].shape[:2] == imgArray[0].shape[:2]:
+                imgArray[x] = cv2.resize(imgArray[x], (0, 0), None, scale, scale)
+            else:
+                imgArray[x] = cv2.resize(imgArray[x], (imgArray[0].shape[1], imgArray[0].shape[0]), None,scale, scale)
+            if len(imgArray[x].shape) == 2: imgArray[x] = cv2.cvtColor(imgArray[x], cv2.COLOR_GRAY2BGR)
+        hor= np.hstack(imgArray)
+        ver = hor
+    return ver
+
+def getContours(img,imgContour):
+    contours, hierarchy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+    
+    # detect the area of each contour and based on the area we can remove all the contours that we are not interested in
+    # so in order to do that we will need a for loop 
+    for cnt in contours:
+        area = cv2.contourArea(cnt)
+        areaMin = cv2.getTrackbarPos("Area", "Parameters")
+        if area > areaMin:
+            cv2.drawContours(imgContour, cnt, -1, (255, 0, 255), 7)
+            
+            # find the corner points, so in order to do that we need to first find the length of contours
+            # true basically means that the contour is closed
+            peri = cv2.arcLength(cnt, True)
+            
+            # to approximate what type of shape this is, we will use the approximation of poly method 
+            # we will input the contour, will give it a resolution and then we will define again that this is a closed contour 
+            approx = cv2.approxPolyDP(cnt, 0.02 * peri, True)
+            print(len(approx))
+            
+            # create a bounding box because we need to highlight the area of where the object is 
+            x , y , w, h = cv2.boundingRect(approx)
+            cv2.rectangle(imgContour, (x , y ), (x + w , y + h ), (0, 255, 0), 5)
+
+            # to display values so that we can easily see the number of points and the area detected
+            cv2.putText(imgContour, "Points: " + str(len(approx)), (x + w + 20, y + 20), cv2.FONT_HERSHEY_COMPLEX, .7, (0, 255, 0), 2)
+            cv2.putText(imgContour, "Area: " + str(int(area)), (x + w + 20, y + 45), cv2.FONT_HERSHEY_COMPLEX, 0.7, (0, 255, 0), 2)
+
+# reading each frame
+while True:
+    success, img = cap.read()
+    imgContour = img.copy()
+    
+    # converting img into blur version
+    imgBlur = cv2.GaussianBlur(img, (7, 7), 1)
+    
+    # converting it into gray scale
+    imgGray = cv2.cvtColor(imgBlur, cv2.COLOR_BGR2GRAY)
+    
+    # define trackbar positions
+    threshold1 = cv2.getTrackbarPos("Threshold1", "Parameters")
+    threshold2 = cv2.getTrackbarPos("Threshold2", "Parameters")
+    
+    # canny edge detector
+    imgCanny = cv2.Canny(imgGray,threshold1,threshold2)
+    
+    # define kernel for dilation
+    kernel = np.ones((5, 5))
+    
+    # to overcome the overlaps and noise, we use dilation function
+    imgDil = cv2.dilate(imgCanny, kernel, iterations=1)
+    getContours(imgDil,imgContour)
+    
+    # stacking images together as we want images side by side instead of different window 
+    imgStack = stackImages(0.8,([img,imgCanny],
+                                [imgDil,imgContour]))
+    
+    # displaying it 
+    cv2.imshow("Result", imgStack)
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break