Improved detection algorithm and made structural changes.

camera module/main.py

@@ -1,19 +1,22 @@
 import cv2
 
 # Constant variables definition.
-DESIRED_HEIGHT = 480	# The input image will be resized to this height, preserving its aspect ratio.
-BLUE_THRESHOLD = 150	# If the blue channel is bigger than this, it is considered background and removed.
-BINARY_THRESHOLD = 1	# If the pixel is not brighter than this, it is removed before detection.
-
-
-# Colors (assuming BGR order).
+DESIRED_HEIGHT = 480		# The input image will be resized to this height, preserving its aspect ratio.
+BLUE_THRESHOLD = 150		# If the blue channel is bigger than this, it is considered background and removed.
+BINARY_THRESHOLD = 1		# If the pixel is not brighter than this, it is removed before detection.
+LINE_THICKNESS = 2			# Thickness of the drawn lines.
+BLUR_KERNEL_SIZE = 3		# The size of the Gaussian blur kernel.
+DILATION_KERNEL_SIZE = 5	# The size of the dilation kernel.
+DILATION_ITERATIONS = 5		# The number of dilation iterations.
+
+# Colors (assuming the default BGR order).
 RED = (0, 0, 255)
 GREEN = (0, 255, 0)
 BLUE = (255, 0, 0)
 YELLOW = (0, 255, 255)
 
 
-# Function definitions
+# -------------- Function definitions -----------------------------
 def resizeImage(img):
 	"Resize the input image based on the DESIRED_HEIGHT variable."
 	p = img.shape;
@@ -22,49 +25,71 @@ def resizeImage(img):
 	img = cv2.resize(img, ( DESIRED_HEIGHT, int(width) ))
 	return img
 
+def processImage(img):
+
+	# Resize image to the desired height.
+	resized = resizeImage(img)
+	
+	dim = resized.shape
+
+	# Remove BLUE
+	noBlue = resized.copy()
+	for i in range(dim[0]):
+		for j in range(dim[1]):
+			if (resized[i,j,0] > BLUE_THRESHOLD):
+				noBlue[i,j,:] = 0
+
+	# Convert to grayscale.
+	gray = cv2.cvtColor(noBlue, cv2.COLOR_BGR2GRAY)
+
+	# Blur the image.
+	blur = cv2.GaussianBlur(gray, (BLUR_KERNEL_SIZE, BLUR_KERNEL_SIZE), 0)
+	
+	# Threshold the image and find its contours.
+	_, imgThres = cv2.threshold(blur, BINARY_THRESHOLD, 255, cv2.THRESH_BINARY)
+	
+	# Dilate the image.
+	dilated = cv2.dilate(imgThres, (DILATION_KERNEL_SIZE,DILATION_KERNEL_SIZE), iterations=DILATION_ITERATIONS)
+	
+	# Find the largest image contour.
+	_, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+	maxContour = max(contours, key = cv2.contourArea)
+
+	'''
+	hull = cv2.convexHull(maxContour)
+	cv2.drawContours(imgOriginal, maxContour, -1, (0,0,255), 3)
+	cv2.drawContours(imgOriginal, hull, -1, (255,0,0), 3)
+	'''
+
+	# Get the bounding rectangle of the contour.
+	x,y,w,h = cv2.boundingRect(maxContour)
+
+	# Get the centroid of the rectangle.
+	objCenterX = int( (x + x + w) / 2)
+	objCenterY = int( (y + y + h) / 2)
+	imgCenterX = int(dim[1]/2)
+	imgCenterY = int(dim[0]/2)
+	
+	# Draw the bounding rectangle  and its centroid to the image.
+	cv2.circle(resized, (objCenterX, objCenterY), 5, YELLOW, LINE_THICKNESS)
+	cv2.rectangle(resized, (x,y), (x+w,y+h), RED, LINE_THICKNESS)
+	
+	return resized
+
 #####################################################################################
 
+
 # Read image from source
 img = cv2.imread('/home/stelios/Desktop/IoT/Talos_Drones_Tracking_and_Telemetry/camera module/drone.jpg', cv2.IMREAD_COLOR)
 
-# Resize image to the desired height.
-img = resizeImage(img)
-
-imgOriginal = img.copy()
-
-# Remove BLUE
-p = img.shape
-for i in range(p[0]):
-	for j in range(p[1]):
-		if (img[i,j,0] > BLUE_THRESHOLD):
-			img[i,j,:] = 0
-
-# Convert to grayscale.
-img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
-
-# Threshold the image and find its contours.
-thres, imgThres = cv2.threshold(img, BINARY_THRESHOLD, 255, cv2.THRESH_BINARY)
-img2, contours, hierarchy = cv2.findContours(imgThres, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
-
-# Find the biggest contour.
-maxContour = max(contours, key = cv2.contourArea)
-
-'''
-hull = cv2.convexHull(maxContour)
-cv2.drawContours(imgOriginal, maxContour, -1, (0,0,255), 3)
-cv2.drawContours(imgOriginal, hull, -1, (255,0,0), 3)
-'''
-
-# Get the bounding rectangle of the contour.
-x,y,w,h = cv2.boundingRect(maxContour)
-
-# Get the centroid of the rectangle.
-centerX = int( (x + x + w) / 2)
-centerY = int( (y + y + h) / 2)
+# Process the image and get the output.
+output = processImage(img)
 
-# Draw the bounding rectangle  and its centroid to the image.
-cv2.circle(imgOriginal, (centerX, centerY), 5, YELLOW, 2)
-cv2.rectangle(imgOriginal, (x,y), (x+w,y+h), RED, 2)
+cv2.imshow('Output', output)
 
-cv2.imshow('Output', imgOriginal)
-cv2.waitKey(0)
+# Terminate if the escape key is pressed or the window is closed.
+while True:
+	k = cv2.waitKey()
+	if k == 27 or k == 255:
+		break
+cv2.destroyAllWindows()