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mpolinowskimpolinowski
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‎.gitignore‎

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venv
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.idea

‎01_load_media.py‎

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import cv2
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# Load and Display an Image 5s
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img = cv2.imread('resources/shenzhen-subway.jpg')
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cv2.imshow("Output", img)
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cv2.waitKey(5000)
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###############################################################
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# Capture a video to variable and display until `q` key is pressed
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cap = cv2.VideoCapture('resources/sz-office.mp4')
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try:
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while True:
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success, vid = cap.read()
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cv2.imshow('Video', vid)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break
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except:
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print("Reached end of video file")
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# Capture video from your webcam
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cap = cv2.VideoCapture(0) #Capture video source zero `/dev/video0`
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cap.set(16, 1920)
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cap.set(9, 1080)
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while True:
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success, vid = cap.read()
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cv2.imshow('Video', vid)
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if cv2.waitKey(1) & 0xFF == ord('q'):
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break

‎02_grayscale_blur_edge_detection.py‎

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import cv2
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import numpy as np
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# Image grayscale, blur and edge detection
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img = cv2.imread('resources/shenzhen-subway.jpg')
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kernel = np.ones((5, 5), np.uint8)
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imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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imgBlur = cv2.GaussianBlur(imgGray, (9, 9), 0)
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imgLowEdge = cv2.Canny(img, 100, 100)
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imgHighEdge = cv2.Canny(img, 200, 300)
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imgDilation = cv2.dilate(imgLowEdge, kernel, iterations=5)
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imgEroded = cv2.erode(imgDilation, kernel, iterations=3)
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cv2.imshow("Grayscale Image", imgGray)
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cv2.imshow("Blurred Image", imgBlur)
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cv2.imshow("Low Edge Threshold Image", imgLowEdge)
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cv2.imshow("High Edge Threshold Image", imgHighEdge)
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cv2.imshow("Increased Edge Thickness", imgDilation)
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cv2.imshow("Decreased Edge Thickness", imgEroded)
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cv2.waitKey(5000)

‎03_joining_images.py‎

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import cv2
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import numpy as np
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# Joining Images in Vertical or Horizontal Stacks
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img = cv2.imread('resources/shenzhen-subway.jpg')
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kernel = np.ones((5, 5), np.uint8)
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imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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imgBlur = cv2.GaussianBlur(imgGray, (9, 9), 0)
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imgLowEdge = cv2.Canny(img, 100, 100)
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imgHighEdge = cv2.Canny(img, 200, 300)
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imgDilation = cv2.dilate(imgLowEdge, kernel, iterations=5)
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imgEroded = cv2.erode(imgDilation, kernel, iterations=3)
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imgVer = np.vstack(( imgGray, imgBlur))
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imgHor = np.hstack((imgLowEdge, imgHighEdge, imgDilation, imgEroded))
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cv2.imshow("Posterized Image", imgHor)
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cv2.imshow("Grayscale Image", imgVer)
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cv2.waitKey(5000)

‎04_cropping_resizing.py‎

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import cv2
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# Image cropping and resizing
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img = cv2.imread('resources/shenzhen-subway.jpg')
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print(img.shape)
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imgResize = cv2.resize(img, (300, 200))
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print(imgResize.shape)
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imgCropped = img[0:250, 624:824] # height,width
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cv2.imshow("Resized Image", imgResize)
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cv2.imshow("Image Crop", imgCropped)
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cv2.waitKey(5000)

‎05_adding_shapes_text.py‎

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import cv2
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import numpy as np
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# Adding Shapes and Text
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img = np.zeros((512, 512, 3), np.uint8) # 512x512 black image background with 3 colour channels BGR
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imageDim = str(img.shape[0]) + ' : ' + str(img.shape[1])
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img[:] = 255, 0, 0 # Colour entire image blue
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img[112:400, 112:400] = 0, 255, 0 # Add a green square in the middle
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# cv2.line(img, (0, 0), (512, 512), (0, 0, 255), 3) # Draw a line from the top left to bottom right
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# cv2.line(img, (512, 0), (0, 512), (0, 0, 255), 3) # Draw a line from the top right to bottom left
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# Do the same thing - but for an image of unknown dimensions
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# with img.shape[1]=width and img.shape[0]=height
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cv2.line(img, (0, 0), (img.shape[1], img.shape[0]), (0, 0, 255), 3)
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cv2.line(img, (img.shape[1], 0), (0, img.shape[0]), (0, 0, 255), 3)
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cv2.rectangle(img, (3, 3), (509, 509), (0, 0, 255), 5) # Draw a rectangle around the image
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cv2.rectangle(img, (212, 212), (300, 300), (0, 0, 255), cv2.FILLED) # Draw square in the middle
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cv2.circle(img, (256, 256), 30, (0, 255, 255), cv2.FILLED) # Add a circle in the middle
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cv2.putText(img, imageDim, (180, 70), cv2.QT_FONT_NORMAL, 1, (255, 255, 255), 1) # Print image dimensions
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cv2.imshow("Image", img)
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cv2.waitKey(5000)

‎06_perspective_transformation.py‎

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import cv2
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import numpy as np
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# Dewarp selections
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img = cv2.imread('resources/sign.jpg')
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width, height = 250, 350
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pts1 = np.float32([[920, 227], [1216, 244], [873, 780], [1182, 809]])
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pts2 = np.float32([[0, 0], [width, 0], [0, height], [width, height]])
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matrix = cv2.getPerspectiveTransform(pts1, pts2)
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imgOutput = cv2.warpPerspective(img, matrix, (width,height))
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cv2.imshow('Image', imgOutput)
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cv2.waitKey(5000)

‎07_colour_selection.py‎

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import cv2
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import numpy as np
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# Color Detection
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from time import time, sleep
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path = 'resources/songhua-river.jpg'
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# Create a hue slider that helps us
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# find the correct colour to select
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def empty(a):
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pass
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cv2.namedWindow('TrackBars') # Create the Window
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cv2.resizeWindow('TrackBars', 640, 240) # Give it a size
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cv2.createTrackbar('Hue Min', 'TrackBars', 0, 179, empty) # Add a slider for min Hue 0-179
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cv2.createTrackbar('Hue Max', 'TrackBars', 179, 179, empty) # Add a slider for max Hue 179
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cv2.createTrackbar('Sat Min', 'TrackBars', 107, 255, empty) # Add a slider for min Saturation 0-255
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cv2.createTrackbar('Sat Max', 'TrackBars', 255, 255, empty) # Add a slider for max Saturation 255
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cv2.createTrackbar('Val Min', 'TrackBars', 180, 255, empty) # Add a slider for min Value 0-255
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cv2.createTrackbar('Val Max', 'TrackBars', 255, 255, empty) # Add a slider for max Value 255
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while True: # Run loop to continuously update from trackbars
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img = cv2.imread(path)
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imgHSV = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
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h_min = cv2.getTrackbarPos('Hue Min', 'TrackBars')
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h_max = cv2.getTrackbarPos('Hue Max', 'TrackBars')
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s_min = cv2.getTrackbarPos('Sat Min', 'TrackBars')
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s_max = cv2.getTrackbarPos('Sat Max', 'TrackBars')
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v_min = cv2.getTrackbarPos('Val Min', 'TrackBars')
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v_max = cv2.getTrackbarPos('Val Max', 'TrackBars')
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print(h_min, h_max, s_min, s_max, v_min, v_max)
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lower = np.array([h_min, s_min, v_min])
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upper = np.array([h_max, s_max, v_max])
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mask = cv2.inRange(imgHSV, lower, upper) # Create a selection mask based on thresholds
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imgSelection = cv2.bitwise_and(img, img, mask=mask) # Apply layer mask to image
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imgHor = np.hstack((img, imgSelection))
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cv2.imshow('Original & Selection', imgHor)
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cv2.imshow('Mask', mask)
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cv2.waitKey(5000)

‎08_contours_shape_detection.py‎

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import cv2
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import numpy as np
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def getContours(img): # Retrieve contours from detected shapes
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contours, hierachy = cv2.findContours(img, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
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for cnt in contours:
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area = cv2.contourArea(cnt) # Get areas for all contours
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# print(area) # Print calculated areas
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if area > 400: # Set threshold to exclude noise
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cv2.drawContours(imgBlack, cnt, -1, (255, 0, 0), 1) # Draw those areas onto the image
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peri = cv2.arcLength(cnt, True) # Get contour perimeter
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approx = cv2.approxPolyDP(cnt, 0.02*peri, True) # Approximate polygonal curve
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# print(len(approx)) # Print the number corner points of each contour
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objCorners = len(approx)
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x, y, w, h = cv2.boundingRect(approx) # Get coordinates from curve
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if objCorners == 3:
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objectType = "Triangle" # Define object based on corner count
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elif objCorners == 4:
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aspectRatio = w/float(h) # Check if w/h=1 => square
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if 0.95 < aspectRatio < 1.05: objectType = "Square"
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else: objectType = "Rectangle"
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elif objCorners == 5:
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objectType = "Pentagon"
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elif objCorners == 6:
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objectType = "Hexagon"
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elif objCorners == 7:
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objectType = "Heptagon"
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elif objCorners > 7:
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objectType = "Circle?"
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else: objectType = "Unknown"
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cv2.rectangle(imgBlack, (x, y), (x+w, y+h), (0, 0, 255, 1)) # Print bounding box
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cv2.putText(imgBlack, objectType,
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(x+(w//2)-10, y+(h//2)-10), # Put objectType in Center
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cv2.QT_FONT_NORMAL, 0.5, (255, 255, 0), 1)
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# Contours and Shape detection
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path = 'resources/objects_dark.png'
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# path = 'resources/objects_light.png'
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img = cv2.imread(path)
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imgBlack = np.zeros_like(img)
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# imgWhite = np.ones_like(img)
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imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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imgBlur = cv2.GaussianBlur(imgGray, (7, 7), 1)
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imgEdge = cv2.Canny(imgBlur, 50, 50)
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getContours(imgEdge)
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cv2.imshow("Shape", imgBlack)
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cv2.imshow("Original", img)
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cv2.waitKey(15000)

‎09_face_detection.py‎

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import cv2
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# Face Detection
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faceCascade = cv2.CascadeClassifier('resources/haarcascade_frontalface_default.xml')
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imagePath = 'resources/hongkong-metro.png'
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img = cv2.imread(imagePath)
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imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # Create grayscale image
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faces = faceCascade.detectMultiScale(imgGray, 1.1, 4) # Detect all faces in image
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for (x, y, w, h) in faces:
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cv2.rectangle(img, (x, y), (x+w, y+h), (0, 0, 255), 2)
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cv2.imshow("Face Detection", img)
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cv2.waitKey(5000)

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