Lecture

OpenCV-Python

02 Aug 2024 • Richard Kuo

Image Processing using OpenCV-Python

Python 程式設計

Python Programming

Python Tutorials

OpenCV 安裝

OpenCV & packages installation

cd ~
pip3 install numpy pandas
pip3 install matplotlib pillow imutils
pip3 install opencv-python
pip3 install opencv-contrib-python
git clone https://github.com/rkuo2000/cv2
cd cv2

Image Processing (影像處理)

read JPEG

python3 jpg_read.jpg
~/cv2/jpg_read.py

import cv2
import sys

if len(sys.argv)>1:
    filename = sys.argv[1]
else:
    filename = 'test.jpg'
	
img = cv2.imread(filename)

print(type(img))

print(img.shape)

cv2.imshow('Image',img)

cv2.waitKey(0)
cv2.DestroyAllWindows()

open Camera

python3 cam.py
~/cv2/cam.py

import cv2
import sys
if len(sys.argv) >1:
    vid = int(sys.argv[1])
else:
    vid = 0
cap = cv2.VideoCapture(vid)
#cap.set(cv2.CAP_PROP_FRAME_WIDTH,1280);
#cap.set(cv2.CAP_PROP_FRAME_HEIGHT,720);

while(cap.isOpened()):
    ret, frame = cap.read()
    print(frame.shape)
    #frame = cv2.flip(frame, 1) # 0: vertical flip, 1: horizontal flip

    cv2.imshow('Camera', frame)
    if cv2.waitKey(10) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

Color system: RGB, CMYK

RGB和CMYK的色彩模式差異

電腦色彩學 – CMYK與RGB 的差異與設定

Color Space
Munsell color system hue (basic color), chroma (color intensity), and value (lightness)

HSL and HSV
Hue, Saturation, Lightness and Hue色調, Saturation 飽和度, Value亮度
HSV色輪允許用戶快速的選擇眾多顏色
HSV模型的圓錐表示適合於在一個單一物體中展示整個HSV色彩空間

OpenCV color conversion

~/cv2/jpg_csc.py

import cv2

img = cv2.imread('test.jpg')
gray= cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)
org1= cv2.cvtColor(gray,cv2.COLOR_GRAY2BGR)
org2= cv2.cvtColor(hsv, cv2.COLOR_HSV2BGR)

cv2.imshow('ORG' ,img)
cv2.imshow('GRAY',gray)
cv2.imshow('HSV' ,hsv)
cv2.imshow('ORG1',org1)
cv2.imshow('ORG2',org2)

cv2.waitKey(0)
cv2.destroyAllWindows()

Object Tracking by Color

~/cv2/cam_object_tracking.py

import cv2
import numpy as np

cap = cv2.VideoCapture(0)

while(1):
    # Take each frame
    _, frame = cap.read()

    # Convert BGR to HSV
    hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)

    # define range of blue color in HSV
    lower_blue = np.array([110,50,50])
    upper_blue = np.array([130,255,255])
	
    # Threshold the HSV image to get only blue colors
    mask = cv2.inRange(hsv, lower_blue, upper_blue)
	
    # Bitwise-AND mask and original image
    res = cv2.bitwise_and(frame, frame, mask=mask)
	
    cv2.imshow('FRAME', frame)
    cv2.imshow('MASK', mask)
    cv2.imshow('RESULT', res)
    k = cv2.waitKey(5) & 0xFF
    if k==27:
        break
	
cv2.destroyAllWindows()

Skin Isolation

~/cv2/cam_skin_detection.py
~/cv2/jpg_skin_detection.py

Image Thresholding

Simple Thresholding: ~/cv2/jpg_image_thresholding.py
- cv2.THRESH_BINARY
- cv2.THRESH_BINARY_INV
- cv2.THRESH_TRUNC
- cv2.THRESH_TOZERO
- cv2.THRESH_TOZERO_INV
Adaptive Thresholding: ~/cv2/jpg_adaptive_thresholding.py
Otsu’s Binarization: ~/cv2/jpg_otsu_binarization.py

Image Processing Tutorial

Smoothing Images

blur()
GaussianBlur()
medianBlur()
bilateralFilter()
filter2D(): jpg_2dfilter.py

Morphological Transformations

Erosion
Dilation
Opening
Closing
Morphological Gradient
Top Hat
Black Hat

~/cv2/jpg_morphological_transformations.py

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('j.png',0)
kernel = np.ones((5,5),np.uint8)
erosion = cv2.erode(img,kernel,iterations = 1)
dilation = cv2.dilate(img,kernel,iterations = 1)
opening = cv2.morphologyEx(img, cv2.MORPH_OPEN, kernel)
closing = cv2.morphologyEx(img, cv2.MORPH_CLOSE, kernel)
gradient = cv2.morphologyEx(img, cv2.MORPH_GRADIENT, kernel)
tophat = cv2.morphologyEx(img, cv2.MORPH_TOPHAT, kernel)
blackhat = cv2.morphologyEx(img, cv2.MORPH_BLACKHAT, kernel)

titles = ['Image','Erosion','Dilation','Opening','Closing','Gradient','Tophat','Blackhat']
images = [img, erosion, dilation, opening, closing, gradient, tophat, blackhat]

for i in range(8):
    plt.subplot(2,4,i+1), plt.imshow(images[i],'gray')
    plt.title(titles[i])
    plt.xticks([]),plt.yticks([])
plt.show()

Geometric Transformation

Scaling (Resize): jpg_resize.py
Rotation : jpg_rotation.py
Affine Transform: jpg_affine_transformation.py
Perspective Transform jpg_perspective_transformation.py

Reference: OpenCV學習筆記】之仿射變換（Affine Transformation）

Image Gradients

~/cv2/jpg_sobel.py

import cv2

org = cv2.imread('test.jpg')
gray  = cv2.cvtColor(org, cv2.COLOR_RGB2GRAY)
img   = cv2.GaussianBlur(gray, (3,3), 0) # remove noise

# convolute with proper kernels
laplacian = cv2.Laplacian(img, cv2.CV_64F)
sobel_x = cv2.Sobel(img, cv2.CV_16S, 1, 0, ksize=5)
sobel_y = cv2.Sobel(img, cv2.CV_16S, 0, 1, ksize=5)
cv2.imshow('Laplacian', laplacian)
cv2.imshow('SobelX', sobel_x)
cv2.imshow('SobelY', sobel_y)

abs_grad_x = cv2.convertScaleAbs(sobel_x)
abs_grad_y = cv2.convertScaleAbs(sobel_y)
grad = cv2.addWeighted(abs_grad_x, 0.5, abs_grad_y, 0.5, 0)    
cv2.imshow('Sobel', grad)

cv2.waitKey(0)
cv2.destroyAllWindows()

Fourier Transform

~/cv2/jpg_fft.py

~/cv2/jpg_dft.py

Why Laplacian/Sobel is a High Pass Filter?
From image, you can see what frequency region each kernel blocks, and what region it passes. From that information, we can say why each kernel is a HPF or a LPF

Edge Detection

Noise Reduction : To remove the noise in the image with a 5x5 Gaussian filter.
Finding Intensity Gradient of the Image
Non-maximum Suppression
Hysteresis Thresholding

邊緣偵測懶人包-Canny演算法

~/cv2/jpg_canny.py

Hough Line Transform

Hough Transform

Hough Lines
~/cv2/jpg_houghlines.py

img = cv2.imread('lanes.jpg', cv2.IMREAD_COLOR) # road.png is the filename
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
edges = cv2.Canny(gray, 50, 200)
# Detect points that form a line
lines = cv2.HoughLinesP(edges, 1, np.pi/180, max_slider, minLineLength=10, maxLineGap=250)

# Draw lines on the image
for line in lines:
    x1, y1, x2, y2 = line[0]
    cv2.line(img, (x1, y1), (x2, y2), (255, 0, 0), 3)

cv2.imshow("Result Image", img)

Hough Circles
~/cv2/jpg_houghcircles.py

img = cv2.imread('circles.png', cv2.IMREAD_COLOR)
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
img_blur = cv2.medianBlur(gray, 5)
circles = cv2.HoughCircles(img_blur, cv2.HOUGH_GRADIENT, 1, img.shape[0]/64, param1=200, param2=10, minRadius=5, maxRadius=30)

# Draw detected circles
if circles is not None:
    circles = np.uint16(np.around(circles))
    for i in circles[0, :]:   
        cv2.circle(img, (i[0], i[1]), i[2], (0, 255, 0), 2) # Draw outer circle       
        cv2.circle(img, (i[0], i[1]), 2, (0, 0, 255), 3) # Draw inner circle

Image Histogram

Gray to Histogram

import cv2
import numpy as np
from matplotlib import pyplot as plt

gray_img = cv2.imread('images/GoldenGateSunset.png', cv2.IMREAD_GRAYSCALE)
cv2.imshow('GoldenGate',gray_img)
hist = cv2.calcHist([gray_img],[0],None,[256],[0,256])
plt.hist(gray_img.ravel(),256,[0,256])
plt.title('Histogram for gray scale picture')
plt.show()

Color to Histogram

import cv2
import numpy as np
from matplotlib import pyplot as plt

img = cv2.imread('images/GoldenGateSunset.png', -1)
cv2.imshow('GoldenGate',img)

color = ('b','g','r')
for channel,col in enumerate(color):
    histr = cv2.calcHist([img],[channel],None,[256],[0,256])
    plt.plot(histr,color = col)
    plt.xlim([0,256])
plt.title('Histogram for color scale picture')
plt.show()

Histogram Equalization
~/cv2/jpg_histogram_equalization.py

img = cv2.imread('test.jpg')
src = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
dst = cv2.equalizeHist(src)
cv2.imshow('Source', src)
cv2.imshow('Equalized', dst)

Histogram Backprojection
~/cv2/jpg_histogram_backprojection.py

Contours

求圖像輪廓 cnts, hierarchy = cv2.findContours(thresh, mode, method)
畫輪廓 cv2.drawContours(img, cnts, contourIdx, color, lineType)
求包覆矩形 (x,y,w,h) = cv2.boundingRect(cnt)
求包覆矩形 box = cv2.minAreaRect(cnt)
求包覆圓形 ((x,y), radius) = cv2.minEnclosingCircle(cnt)
求包覆橢圓形 ellipse = cv2.fitEllipse(cnt)
計算輪廓面積 area = cv2.contourArea(cnt)
contours, hierarchy = cv2.findContours(thresh, mode, method)
- img : output image
- contours：包含所有輪廓的容器(vector)，每個輪廓都是儲存點的容器(vector)，所以contours的資料結構為vector< vector>。
- hierarchy：可有可無的輸出向量，以階層的方式記錄所有輪廓
- thresh：輸入圖，使用八位元單通道圖，所有非零的像素都會列入考慮，通常為二極化後的圖
- mode：取得輪廓的模式
- cv2.RETR_EXTERNAL：只取最外層的輪廓。
- cv2.RETR_LIST：取得所有輪廓，不建立階層(hierarchy)。
- cv2.RETR_CCOMP：取得所有輪廓，儲存成兩層的階層，首階層為物件外圍，第二階層為內部空心部分的輪廓，如果更內部有其餘物件，包含於首階層。
- cv2.RETR_TREE：取得所有輪廓，以全階層的方式儲存。
- method：儲存輪廓點的方法
- cv2.CHAIN_APPROX_NONE：儲存所有輪廓點。
- cv2.CHAIN_APPROX_SIMPLE：對水平、垂直、對角線留下頭尾點，所以假如輪廓為一矩形，只儲存對角的四個頂點。
cv2.drawContours(image, contours, contourIdx, color, lineType)
- image：輸入輸出圖，會將輪廓畫在此影像上
- contours：包含所有輪廓的容器(vector)，也就是findContours()所找到的contours
- contourIdx：指定畫某個輪廓 (-1 = all)
- color：繪製的顏色 (0,0,255) in G-B-R
- lineType：繪製的線條型態
Examples:

Hand Contour

~/cv2/jpg_contour_hand.py

img_path = "hand.jpg"
img = cv.imread(img_path)

# define the upper and lower boundaries of the HSV pixel intensities 
# to be considered 'skin'
hsvim = cv.cvtColor(img, cv.COLOR_BGR2HSV)
lower = np.array([0, 48, 80], dtype="uint8")
upper = np.array([20, 255, 255], dtype="uint8")
skinMask= cv.inRange(hsvim, lower, upper)

# blur the mask to help remove noise
skinMask= cv.blur(skinMask, (2, 2))

# get threshold image
ret, thresh = cv.threshold(skinMask, 100, 255, cv.THRESH_BINARY)
cv.imshow("thresh", thresh)

# draw the contours on the empty image
contours, hierarchy = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
contours = max(contours, key=lambda x: cv.contourArea(x))
cv.drawContours(img, [contours], -1, (255, 255, 0), 2)
cv.imshow("contours", img)

cv.waitKey()

Hand Detection and Finger Counting

Hand Detection & Gesture Recognition

pip install cvzone

import cvzone
import cv2

cap = cv2.VideoCapture(0)
cap.set(3, 1280)
cap.set(4, 720)
detector = cvzone.HandDetector(detectionCon=0.5, maxHands=1)

while True:
    # Get image frame
    success, img = cap.read()

    # Find the hand and its landmarks
    img = detector.findHands(img)
    lmList, bbox = detector.findPosition(img)
    
    # Display
    cv2.imshow("Image", img)
    cv2.waitKey(1)

Graph Segmentation

Color Matching

~/cv2/color_matching_histogram.py

~/cv2/color_matching_meanstddev.py

Face Detection using Cascade Classifier

~/cv2/jpg_face_detect.py

if len(sys.argv)>1:
    img = cv2.imread(sys.argv[1])
else:
    img = cv2.imread("friends.jpg")
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
face = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
bboxes = face.detectMultiScale(gray)

for box in bboxes:
    print(box)
    (x,y,w,h) = tuple(box)
    img = cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)

cv2.imshow('img',img)

Face Recognizer

EigenFaces Face Recognizer
FisherFaces Face Recognizer
Local Binary Patterns Histograms (LBPH) Face Recognizer

LBPH Face Recognition

git clone https://github.com/informramiz/opencv-face-recognition-python
cd opencv-face-recognition-python
python3 OpenCV-Face-Recognition-Python.py

Object Tracking

Tracker: csrt, kcf, boosting, mil, tld, medianflow, mosse

csrt for slower FPS, higher object tracking accuracy
kcf for faster FPS, lower object tracking accuracy
mosse for fastest FPS

Tracking multiple objects with OpenCV
~/cv2/multi_object_tracking.py

Optical Flow

Introduction to Motion Estimation with Optical Flow

What is optical flow?

The problem of optical flow may be expressed as:
where between consecutive frames, we can express the image intensity (I) as a function of space (x,y) and time (t).

First, we assume that pixel intensities of an object are constant between consecutive frames.
Second, we take the Taylor Series Approximation of the RHS and remove common terms.
Third, we divide by dt to derive the optical flow equation: where u = dx/dt, v = dy/dt

To track the motion of vehicles across frames

~/cv2/drone_optical_flow.py

Applications

Motion Detector

Basic motion detection and tracking with Python and OpenCV
~/cv2/cam_motion_detection.py

Webcam Pulse Detector

git clone https://github.com/thearn/webcam-pulse-detector
cd webcam-pulse-detector
python get_pulse.py

Distance Measurement

Blog: Real-time Distance Measurement Using Single Image

OCR (Optical Character Recognition)

Tesseract OCR
- Tesseract Documentation
- Tesseract installers for Windows
  - tesseract-ocr-w32-setup-v5.2.0.20220712.exe (32 bit)
  - tesseract-ocr-w64-setup-v5.2.0.20220712.exe (64 bit)

Blog: Open OCR and text recognition with Tesseract

Blog: Improve Accuracy of OCR using Image Preprocessing
Scaling Image

Skew Correction

~cv2/ocr_skew_correction.py

QR Scanner

安裝函式庫 pip install pyzbar
QR code 產生器：~/cv2/qr_gen.html
~/cv2/qr_scan.py
- python qr_scan.py -i qr_csu.jpg

樂譜辨識

cadenCV is an optical music recognition system written in the Python programming language which reads sheet music and sequences a MIDI file for user playback.

git clone https://github.com/afikanyati/cadenCV
cd cadenCV
rm output/*
pip install midiutil
python main.py resources/samples/mary.jpg

Output: .jpg & .midi

html-midi player : upload output.mid & play

This site was last updated November 15, 2024.