Computer Vision
===
###### tags: `IvLabs`

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Aaron Bobick
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- Lecture 1-5

OpenCV Documentation
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- Reading and Displaying Image

img = cv2.imread('apple.webp')
cv2.imshow('apple', img)
- Reading and Displaying Video

video = cv2.VideoCapture(0)
while True:
ret, img = video.read()
cv2.imshow("Camera", img)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
- BGR to Grayscale

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
- BGR to HSV

imghsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
- Gaussian Blur

imgBlur = cv2.GaussianBlur(result, (7, 7), 1)
- Canny Edge Detection

imgCanny = cv2.Canny(imgGray, 100, 200)
- Image Dialation

imgDil = cv2.dilate(imgCanny, kernel, iterations=1)
- Image Erosion

image = cv2.erode(image, kernel, iterations=1)
- Creating Trackbar

def empty(x):
pass

cv2.namedWindow("HSV Value")
cv2.resizeWindow("HSV Value", 360, 240)

cv2.createTrackbar("Hue min", "HSV Value", 0, 179,empty)
- Getting Trackbar Value
(Inside Loop)

h_min = cv2.getTrackbarPos("Hue min", "HSV Value")

Youtube(Murtaza's Workshop)
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- https://www.youtube.com/@murtazasworkshop/playlists

- Finding Contours and Centre:

def getContours(img, imgcnt):
cnt, hier = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

for c in cnt:
area = cv2.contourArea(c)
if area>500 :
cv2.drawContours(imgcnt, c, -1, (0,0,255), 5)
perimeter = cv2.arcLength(c, True)
edges = cv2.approxPolyDP(c, 0.02*perimeter, True)
x, y, w, h = cv2.boundingRect(edges)

cv2.rectangle(imgcnt, (x, y), (x+w, y+h), (0, 255, 0), 5)
M = cv2.moments(c)
if M['m00'] != 0 :
cx = int(M['m10']/M['m00'])
cy = int(M['m01']/M['m00'])
cv2.circle(imgcnt, (cx, cy), 5, (255, 0, 0), 3)

- Creating a mask:

lower = np.array([h_min, s_min, v_min])
upper = np.array([h_max, s_max, v_max])

filt = cv2.inRange(imghsv, lower, upper)
result = cv2.bitwise_and(img, img, mask=filt)