Case Studies

1. Introduction
2. Face Detection
3. Webcam Face Detection
4. Object Tracking in Video
5. Eye Tracking
6. Handwriting Recognition with HOG
7. Plant Classification
8. Building an Amazon.com Cover Search
9. Conclusions

Load Display Save

import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())
#load
image = cv2.imread(args["image"])
w, h, c = image.shape
#display
cv2.imshow("Image", image)
cv2.waitKey(0)
#save
cv2.imwrite("newimage.jpg", image)

Drawing

import numpy as np
import cv2
canvas = np.zeros((300, 300, 3), dtype = "uint8")
#line
green = (0, 255, 0)
cv2.line(canvas, (0, 0), (300, 300), green)
cv2.imshow("Canvas", canvas)
cv2.waitKey(0)
red = (0, 0, 255)
cv2.line(canvas, (300, 0), (0, 300), red, 3)
cv2.imshow("Canvas", canvas)
cv2.waitKey(0)
#rectangle
cv2.rectangle(canvas, (10, 10), (60, 60), green)
cv2.imshow("Canvas", canvas
cv2.waitKey(0)
cv2.rectangle(canvas, (50, 200), (200, 225), red, 5)
cv2.imshow("Canvas", canvas)
cv2.waitKey(0)
blue = (255, 0, 0)
cv2.rectangle(canvas, (200, 50), (225, 125), blue, -1)
cv2.imshow("Canvas", canvas)
cv2.waitKey(0)
#circle 
canvas = np.zeros((300, 300, 3), dtype = "uint8")
(centerX, centerY) = (canvas.shape[1] // 2, canvas.shape[0] // 2)
white = (255, 255, 255)
for r in range(0, 175, 25):
    cv2.circle(canvas, (centerX, centerY), r, white)
cv2.imshow("Canvas", canvas)
cv2.waitKey(0)
#abstract drawing
for i in range(0, 25):
    radius = np.random.randint(5, high = 200)
    color = np.random.randint(0, high = 256, size = (3,)).tolist()
    pt = np.random.randint(0, high = 300, size = (2,))
    cv2.circle(canvas, tuple(pt), radius, color, -1) 
cv2.imshow("Canvas", canvas)
cv2.waitKey(0)

Image Processing

translation

import numpy as np
import argparse
import imutils
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", required = True, help = "Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
cv2.imshow("Original", image)
M = np.float32([[1, 0, 25], [0, 1, 50]])
shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
cv2.imshow("Shifted Down and Right", shifted)
M = np.float32([[1, 0, -50], [0, 1, -90]])
shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
cv2.imshow("Shifted Up and Left", shifted)

rotation

import numpy as np
import argparse
import imutils
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default='33.jpg', help = "Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
cv2.imshow("Original", image)
(h, w) = image.shape[:2]
center=(w//2,h//2)
M = cv2.getRotationMatrix2D(center, 45, 1.0)
rotated = cv2.warpAffine(image, M, (w, h))
cv2.imshow("Rotated by 45 Degrees", rotated)
M = cv2.getRotationMatrix2D(center, -90, 1.0)
rotated = cv2.warpAffine(image, M, (w, h))
cv2.imshow("Rotated by -90 Degrees", rotated)
cv2.waitKey()

resizing

import numpy as np
import argparse
import imutils
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default='33.jpg', help = "Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
cv2.imshow("Original", image)
r = 150.0 / image.shape[1]
dim = (150, int(image.shape[0] * r))
resized = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
cv2.imshow("Resized (Width)", resized)
r = 50.0 / image.shape[0]
dim = (int(image.shape[1] * r), 50)
resized = cv2.resize(image, dim, interpolation = cv2.INTER_AREA)
cv2.imshow("Resized (Height)", resized)
cv2.waitKey(0)

flipping

import argparse
import cv2
ap = argparse.ArgumentParser()
ap.add_argument("-i", "--image", default='33.jpg', help = "Path to the image")
args = vars(ap.parse_args())
image = cv2.imread(args["image"])
cv2.imshow("Original", image)
flipped = cv2.flip(image, 1)
cv2.imshow("Flipped Horizontally", flipped)
flipped = cv2.flip(image, 0)
cv2.imshow("Flipped Vertically", flipped)
flipped = cv2.flip(image, -1)
cv2.imshow("Flipped Horizontally & Vertically", flipped)
cv2.waitKey(0)

Histograms

Smoothing and Blurring

Thresholding

Gradients and Edge Detection

Contours