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Made further improvements to the detection algorithm.

master
Stelios Giakoumidis 5 years ago
parent
commit
5546a0f5cc
  1. 140
      camera module/main.py

140
camera module/main.py

@ -3,11 +3,16 @@ import cv2
# Constant variables definition. # Constant variables definition.
DESIRED_HEIGHT = 480 # The input image will be resized to this height, preserving its aspect ratio. 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. 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. BINARY_THRESHOLD = 30 # If the pixel is not brighter than this, it is removed before detection.
CANNY_LOW_THRES = 150 # Low threshold for the canny edge detector.
CANNY_HIGH_THRES = 350 # High threshold for the canny edge detector.
LINE_THICKNESS = 2 # Thickness of the drawn lines. LINE_THICKNESS = 2 # Thickness of the drawn lines.
MIN_CONTOUR_AREA = 100 # Min area of a contour to be considered valid.
MAX_CONTOUR_AREA = 2100 # Max area of a contour to be considered valid.
BLUR_KERNEL_SIZE = 3 # The size of the Gaussian blur kernel. BLUR_KERNEL_SIZE = 3 # The size of the Gaussian blur kernel.
DILATION_KERNEL_SIZE = 5 # The size of the dilation kernel. DILATION_KERNEL_SIZE = 5 # The size of the dilation kernel.
DILATION_ITERATIONS = 5 # The number of dilation iterations. DILATION_ITERATIONS = 5 # The number of dilation iterations.
MIN_DISTANCE_FOR_MOVE = 10 # Min distance of the drone from the center for the servos to move.
# Colors (assuming the default BGR order). # Colors (assuming the default BGR order).
RED = (0, 0, 255) RED = (0, 0, 255)
@ -15,51 +20,85 @@ GREEN = (0, 255, 0)
BLUE = (255, 0, 0) BLUE = (255, 0, 0)
YELLOW = (0, 255, 255) YELLOW = (0, 255, 255)
# -------------- Function definitions ----------------------------- # -------------- Function definitions -----------------------------
def resizeImage(img): def resizeImage(img):
"Resize the input image based on the DESIRED_HEIGHT variable." "Resize the input image based on the DESIRED_HEIGHT variable."
p = img.shape; p = img.shape
aspectRatio = p[0]/p[1] aspectRatio = p[0]/p[1]
width = DESIRED_HEIGHT*aspectRatio width = DESIRED_HEIGHT*aspectRatio
img = cv2.resize(img, ( DESIRED_HEIGHT, int(width) )) img = cv2.resize(img, ( DESIRED_HEIGHT, int(width) ))
return img return img
def removeColors(img):
out = None
dim = img.shape
blue = img.copy()
for i in range(dim[0]):
for j in range(dim[1]):
pixel = img[i,j]
if pixel[0] > 150:
blue[i,j,:] = 255
else:
blue[i,j,:] = 0
gray = cv2.cvtColor(blue, cv2.COLOR_BGR2GRAY)
_, contours, hierarchy = cv2.findContours(gray, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
maxContour = max(contours, key = cv2.contourArea)
x,y,w,h = cv2.boundingRect(maxContour)
out = img[y:y+h,x:x+w,:]
return out
def findMatchingContour(img, objX, objY):
dilated = img.copy()
#dilated = cv2.dilate(img, (5,5), iterations=1)
canny = cv2.Canny(dilated, CANNY_LOW_THRES, CANNY_HIGH_THRES)
_, contours, hierarchy = cv2.findContours(canny, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)
#print('len:' + str(len(contours)))
contours.sort(key = cv2.contourArea, reverse = True)
cv2.imshow('hey', canny)
for i in range(len(contours)):
contour = contours[i]
x,y,w,h = cv2.boundingRect(contour)
area = w*h
dist = cv2.pointPolygonTest(contour, (objX,objY), False)
#print('dist: ' + str(dist))
if area >= MIN_CONTOUR_AREA and area <= MAX_CONTOUR_AREA and dist >= 0:
return (True, contour)
return (False, None)
def processImage(img): def processImage(img):
# Resize image to the desired height. # Resize image to the desired height.
resized = resizeImage(img) resized = resizeImage(img)
#return removeColors(resized)
dim = resized.shape 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. # Convert to grayscale.
gray = cv2.cvtColor(noBlue, cv2.COLOR_BGR2GRAY) gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
# Blur the image. # Blur the image.
blur = cv2.GaussianBlur(gray, (BLUR_KERNEL_SIZE, BLUR_KERNEL_SIZE), 0) blur = cv2.GaussianBlur(gray, (BLUR_KERNEL_SIZE, BLUR_KERNEL_SIZE), 0)
# Threshold the image and find its contours. # Threshold the image and find its contours.
_, imgThres = cv2.threshold(blur, BINARY_THRESHOLD, 255, cv2.THRESH_BINARY) _, imgThres = cv2.threshold(blur, BINARY_THRESHOLD, 255, cv2.THRESH_BINARY_INV)
# Dilate the image. # Dilate the image.
dilated = cv2.dilate(imgThres, (DILATION_KERNEL_SIZE,DILATION_KERNEL_SIZE), iterations=DILATION_ITERATIONS) dilated = cv2.dilate(imgThres, (DILATION_KERNEL_SIZE,DILATION_KERNEL_SIZE), iterations=DILATION_ITERATIONS)
# Find the largest image contour. # Find the largest image contour.
_, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE) _, contours, hierarchy = cv2.findContours(dilated, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)
if len(contours) > 0:
maxContour = max(contours, key = cv2.contourArea) maxContour = max(contours, key = cv2.contourArea)
else:
''' print('No contours found.')
hull = cv2.convexHull(maxContour) return (resized, 0, 0)
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. # Get the bounding rectangle of the contour.
x,y,w,h = cv2.boundingRect(maxContour) x,y,w,h = cv2.boundingRect(maxContour)
@ -69,27 +108,66 @@ def processImage(img):
objCenterY = int( (y + y + h) / 2) objCenterY = int( (y + y + h) / 2)
imgCenterX = int(dim[1]/2) imgCenterX = int(dim[1]/2)
imgCenterY = int(dim[0]/2) imgCenterY = int(dim[0]/2)
#cv2.circle(resized, (objCenterX, objCenterY), 5, BLUE, LINE_THICKNESS)
ret, finalContour = findMatchingContour(blur, objCenterX, objCenterY)
if (ret == False):
return (resized, 0, 0)
#cv2.fillPoly(resized, finalContour, BLUE, cv2.LINE_4)
x,y,w,h = cv2.boundingRect(finalContour)
objCenterX = int( (x + x + w) / 2)
objCenterY = int( (y + y + h) / 2)
# Draw the bounding rectangle and its centroid to the image. # Draw the bounding rectangle and its centroid to the image.
cv2.circle(resized, (objCenterX, objCenterY), 5, YELLOW, LINE_THICKNESS) #cv2.circle(resized, (objCenterX, objCenterY), 5, YELLOW, LINE_THICKNESS)
cv2.rectangle(resized, (x,y), (x+w,y+h), RED, LINE_THICKNESS) cv2.rectangle(resized, (x,y), (x+w,y+h), RED, LINE_THICKNESS)
return resized # Determinate the direction of the object relative to the center of the camera.
xDir, yDir = determinateDir(imgCenterX, imgCenterY, objCenterX, objCenterY)
return (resized, xDir, yDir)
def determinateDir(cenX, cenY, objX, objY):
xDir = 0
yDir = 0
if abs(cenX - objX) >= MIN_DISTANCE_FOR_MOVE:
if objX > cenX:
xDir = 1
else:
xDir = -1
if abs(cenY - objY) >= MIN_DISTANCE_FOR_MOVE:
if objY > cenY:
yDir = -1
else:
yDir = 1
return (xDir, yDir)
##################################################################################### #####################################################################################
# Read image from source cap = cv2.VideoCapture('/home/stelios/Desktop/drone_flight_test (cut).mp4')
img = cv2.imread('/home/stelios/Desktop/IoT/Talos_Drones_Tracking_and_Telemetry/camera module/drone.jpg', cv2.IMREAD_COLOR) if (cap.isOpened() == False):
print('Error opening stream.')
quit()
# Process the image and get the output. #cap.set(1, 30*6)
output = processImage(img)
cv2.imshow('Output', output) while(cap.isOpened()):
ret, frame = cap.read()
if (ret == True):
img, xDir, yDir = processImage(frame)
cv2.imshow('Frame', img)
print('Got ' + str(xDir) + ' ' + str(yDir))
k = cv2.waitKey(25) & 0xFF
if k == 27:
break
if k == ord('p') or k == ord('P'):
cv2.waitKey(0)
# Terminate if the escape key is pressed or the window is closed. else:
while True:
k = cv2.waitKey()
if k == 27 or k == 255:
break break
cap.release()
cv2.destroyAllWindows() cv2.destroyAllWindows()
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