Practical Aspects of OpenCV

import cv2
import math

path = 'test.jpg'
img = cv2.imread(path)
pointsList = []

def mousePoints(event,x,y,flags,params):
if event == cv2.EVENT_LBUTTONDOWN:
size = len(pointsList)
if size != 0 and size % 3 != 0:
cv2.line(img,tuple(pointsList[round((size-1)/3)*3]),(x,y),(0,0,255),2)
cv2.circle(img,(x,y),5,(0,0,255),cv2.FILLED)
pointsList.append([x,y])

def gradient(pt1,pt2):
return (pt2[1]-pt1[1])/(pt2[0]-pt1[0])

def getAngle(pointsList):
pt1, pt2, pt3 = pointsList[-3:]
m1 = gradient(pt1,pt2)
m2 = gradient(pt1,pt3)
angR = math.atan((m2-m1)/(1+(m2*m1)))
angD = round(math.degrees(angR))
cv2.putText(img,str(angD),(pt1[0]-40,pt1[1]-20),cv2.FONT_HERSHEY_COMPLEX,
1.5,(0,0,255),2)


while True:
if len(pointsList) % 3 == 0 and len(pointsList) !=0:
getAngle(pointsList)


cv2.imshow('Image',img)
cv2.setMouseCallback('Image',mousePoints)
if cv2.waitKey(1) & 0xFF == ord('q'):
pointsList = []
img = cv2.imread(path)

Thresholding, Binarization & Adaptive Thresholding

# Load our new image
image = cv2.imread('imagePath', 0)

plt.figure(figsize=(30, 30))
plt.subplot(3, 2, 1)
plt.title("Original")
plt.imshow(image)

# Values below 127 goes to 0 (black, everything above goes to 255 (white)
ret,thresh1 = cv2.threshold(image, 127, 255, cv2.THRESH_BINARY)

plt.subplot(3, 2, 2)
plt.title("Threshold Binary")
plt.imshow(thresh1)


# It's good practice to blur images as it removes noise
image = cv2.GaussianBlur(image, (3, 3), 0)

# Using adaptiveThreshold
thresh = cv2.adaptiveThreshold(image, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 3, 5)

plt.subplot(3, 2, 3)
plt.title("Adaptive Mean Thresholding")
plt.imshow(thresh)


_, th2 = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)

plt.subplot(3, 2, 4)
plt.title("Otsu's Thresholding")
plt.imshow(th2)


plt.subplot(3, 2, 5)
# Otsu's thresholding after Gaussian filtering
blur = cv2.GaussianBlur(image, (5,5), 0)
_, th3 = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
plt.title("Guassian Otsu's Thresholding")
plt.imshow(th3)
plt.show()

Edge Detection & Image Gradients

Identifiy Contours by Shape

image = cv2.imread('sampleImage')
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)

gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)

plt.figure(figsize=(20, 20))

plt.subplot(2, 2, 1)
plt.title("Original")
plt.imshow(image)

ret, thresh = cv2.threshold(gray, 127, 255, 1)

# Extract Contours
contours, hierarchy = cv2.findContours(thresh.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE)

for cnt in contours:

# Get approximate polygons
approx = cv2.approxPolyDP(cnt, 0.01*cv2.arcLength(cnt,True),True)

if len(approx) == 3:
shape_name = "Triangle"
cv2.drawContours(image,[cnt],0,(0,255,0),-1)

# Find contour center to place text at the center
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)

elif len(approx) == 4:
x,y,w,h = cv2.boundingRect(cnt)
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])

# Check to see if 4-side polygon is square or rectangle
# cv2.boundingRect returns the top left and then width and
if abs(w-h) <= 3:
shape_name = "Square"

# Find contour center to place text at the center
cv2.drawContours(image, [cnt], 0, (0, 125 ,255), -1)
cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)
else:
shape_name = "Rectangle"

# Find contour center to place text at the center
cv2.drawContours(image, [cnt], 0, (0, 0, 255), -1)
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)

elif len(approx) == 10:
shape_name = "Star"
cv2.drawContours(image, [cnt], 0, (255, 255, 0), -1)
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)



elif len(approx) >= 15:
shape_name = "Circle"
cv2.drawContours(image, [cnt], 0, (0, 255, 255), -1)
M = cv2.moments(cnt)
cx = int(M['m10'] / M['m00'])
cy = int(M['m01'] / M['m00'])
cv2.putText(image, shape_name, (cx-50, cy), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 0), 2)

plt.subplot(2, 2, 2)
plt.title("Identifying Shapes")
plt.imshow(image)

Scale Invariant Feature Transform (SIFT)

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