jimmyhhua/opencv_tutorials

Opencv4.0 with python (English&中文)， and will keep the update ! 👊

OpenCV 4.0 Tutorial

✒️ 中文版本

Introduction

This repository contains source code of OpenCV Tutorial application, the environment is python3.0 and opencv4.0.

Sample

• Image load

import cv2

src = cv2.imread("test.png")
cv2.namedWindow("input", cv2.WINDOW_AUTOSIZE)
cv2.imshow("input", src)
cv2.waitKey(0)
cv2.destroyAllWindows()

• Gray Image

gray = cv2.cvtColor(src, cv.COLOR_BGR2GRAY)

More opencv4.0 tutorials plese follow the learning road as below 👇👇👇

Learning Road ⛳️

Annotation:

• ✔️ : Basic
• ✏️ : Attention
• ❣️ : Important

No	Description	Annotation
code_001	Load Image	✔️
code_002	Gray Image	✔️
code_003	Image Create	✔️
code_004	Pixel Read and Write	✔️
code_005	Image Pixel Arithmetic Operations	✔️
code_006	Image Pseudo-Color Enhancement	✔️
code_007	Image Pixel Operation (Logical Operation)	✔️
code_008	Image Channel Separation and Merging	✔️
code_009	Color Space Conversion	✏️
code_010	Image Pixel Value Statistics	✔️
code_011	Image Pixel Normalization	✔️
code_012	Video Read and Write	✔️
code_013	Image Flip	✔️
code_014	Image Interpolation	✔️
code_015	Draw Geometry	✔️
code_016	ROI of Image	✔️
code_017	Image Histogram	✔️
code_018	Histogram Dqualization	✏️
code_019	Histogram Comparison	✔️
code_020	Histogram Backprojection	✔️
code_021	Image Convolution	✔️
code_022	Averaging and Gaussian Blur	❣️
code_023	Median Blur	✔️
code_024	Image Noise	✔️
code_025	Smoothing Images	✔️
code_026	Gaussian Bilateral Blur	✔️
code_027	Mean-shift Blur)	✔️
code_028	Image Integral Algorithm	✔️
code_029	Fast Image Edge Filtering Algorithm	✔️
code_030	Custom Filter	✔️
code_031	Sobel Operator	✔️
code_032	More Gradient Operators	✔️
code_033	Laplace Operator	✔️
code_034	Image Sharpening	✔️
code_035	USM Sharpen Algorithm	✔️
code_036	Canny Edge Detection	❣️
code_037	Image Pyramid	✔️
code_038	Laplace Pyramid	✔️
code_039	Image Template Matching	✔️
code_040	Binary introduction	✔️
code_041	Basic Thresholding	✔️
code_042	OTSU Thresholding	✏️
code_043	TRIANGLE Thresholding	✔️
code_044	Adaptive Thresholding	✏️
code_045	Binary and Smoothing	✏️
code_046	Image Connectivity component	✔️
code_047	Image Connected component state statistics	✔️
code_048	Image Contours	❣️
code_049	Bounding Rectangle	❣️
code_050	Contour Area and Perimeter	✏️
code_051	Contour Approximation	✔️
code_052	Contour Centroid Calculate	✔️
code_053	HuMoment for Contour Matching	✔️
code_054	Contour Cricle and Ellipse fitting	✔️
code_055	Convex Hull	✏️
code_056	Fitting a Line	✔️
code_057	Point Polygon Test	✔️
code_058	The Largest Inner Circle	✔️
code_059	Hoffman Line Detection	✔️
code_060	Probability Hoffman Line Detection	❣️
code_061	Hoffman Cricle Detection	❣️
code_062	Dilation and Erosion	❣️
code_063	Structuring Element	✔️
code_064	Opening Transformation	✏️
code_065	Closing Transformation	✏️
code_066	Application of Opening and Closing Operations	✏️
code_067	Top Hat	✔️
code_068	Black Hat	✔️
code_069	Morph Gradient	✔️
code_070	Contour based on Morph Gradient	✏️
code_071	Hit and Miss	✔️
code_072	Defect Detecting-1	✔️
code_073	Defect Detecting-2	✔️
code_074	Extract the Maximum Contour and Coding Key Points	✔️
code_075	Image Inpainting	✔️
code_076	Perspective Transformation	✏️
code_077	Video Read, Write and Process	✏️
code_078	Identify and Track Specific Color Objects in Video	✔️
code_079	Video Analysis-Background/Foreground Extraction	✔️
code_080	Video Analysis–Background Subtraction and ROI Extraction of the Foreground	✔️
code_081	Corner Detection-Harris	✔️
code_082	Corner Detection-Shi-Tomas	✏️
code_083	Corner Detection-Sub-Pixel	✔️
code_084	Video Analysis-KLT Optical Flow-1	✏️
code_085	Video Analysis-KLT Optical Flow-2	✏️
code_086	Video Analysis-Dense Optical Flow	✏️
code_087	Video Analysis-Frame Difference Moving Object Analysis	✔️
code_088	Video Analysis-Meanshift	✏️
code_089	Video Analysis-CamShift	✏️
code_090	Video Analysis-Object Movement Trajectory Drawing	✔️
code_091	Object Detection-HAAR Cascade Classification	✔️
code_092	Object Detection-HAAR Feature Analysis	✔️
code_093	Object Detection-LBP Feature Analysis	✔️
code_094	ORB Feature Critical Point Detection	✏️
code_095	ORB Feature Descriptor Matching	✔️
code_096	Multiple Descriptor Matching Methods	✏️
code_097	Location of Known Objects Based on Descriptor Matches	✏️
code_098	SIFT Feature Critical Point Detection	✔️
code_099	SIFT Feature Descriptor Matching	✔️
code_100	HOG Pedestrian Detection	✔️
code_101	HOG Multiscale Detection	✏️
code_102	HOG Extract Descriptor	✔️
code_103	HOG Use Descriptors to Generate Sample Data	✔️
code_104	(Detection Case)-HOG+SVM Train	✔️
code_105	(Detection Case)-HOG+SVM Predict	✔️
code_106	AKAZE Features and Descriptors	✔️
code_107	Brisk Features and Descriptors	✔️
code_108	GFTT Detector	✔️
code_109	BLOB Feature Analysis	✔️
code_110	KMeans Data Classification	✔️
code_111	KMeans Image Segmentation	✔️
code_112	KMeans Background Change	✔️
code_113	KMeans Extract Image Color Card	✔️
code_114	KNN Classification	✔️
code_115	KNN-Train Data Save and Load	✔️
code_116	Decision Tree Algorithm	✔️
code_117	Image Mean-shift Segmentation	✔️
code_118	Grabcut-Image Segmentation	✔️
code_119	Grabcut-Background Change	✏️
code_120	Qrcode detect and decode	✏️
code_121	DNN- Read the information of each layer of the model	✔️
code_122	DNN- Realize image classification	✔️
code_123	DNN- Model runs to set the target device and compute the background	✔️
code_124	DNN- SSD Single Image Detection	✔️
code_125	DNN- SSD Real-time Video Detection	✔️
code_126	DNN- Face Detection based on Residual Network	✔️
code_127	DNN- Video Face Detection based on Residual Network	✔️
code_128	DNN- Call the Detection Model of Tensorflow	✔️
code_129	DNN- Call the Openpose Implementation Attitude Assessment	✔️
code_130	DNN- Call YOLO Object Detection Network	✔️
code_131	DNN- YOLOv3-tiny Real-time Object Detection	✔️
code_132	DNN- Single and Multiple Image Detection	✔️
code_133	DNN- Colorful Image Colorization	✔️
code_134	DNN- ENet Image Segmentation	✔️
code_135	DNN- Real-time Fast Image Style Transfer	✔️

---

Appendix

⛳️ The weight can be download from Google Driver：

🌱 Weight for DNN

opencv_tutorials's Issues

Is there any assistance needed ?

Hi everyone,
I would love to contribute to this project, however I am not sure if maintainers are still active here ... 🤔
Would appreciate your answer.

“gray = cv2.cv2tColor(src, cv2.COLOR_BGR2GRAY)”代码有误

应为‘’‘gray = cv2.cvtColor(src, cv2.COLOR_BGR2GRAY)’’‘

关于hog问题

我在opencv官方文档里面找到的代码是

def hog(img):
    gx = cv.Sobel(img, cv.CV_32F, 1, 0)
    gy = cv.Sobel(img, cv.CV_32F, 0, 1)
    mag, ang = cv.cartToPolar(gx, gy)
    bins = np.int32(bin_n*ang/(2*np.pi))    # quantizing binvalues in (0...16)
    bin_cells = bins[:10,:10], bins[10:,:10], bins[:10,10:], bins[10:,10:]
    mag_cells = mag[:10,:10], mag[10:,:10], mag[:10,10:], mag[10:,10:]
    hists = [np.bincount(b.ravel(), m.ravel(), bin_n) for b, m in zip(bin_cells, mag_cells)]
    hist = np.hstack(hists)     # hist is a 64 bit vector
    return hist

deskewed = [list(map(deskew,row)) for row in train_cells]
hogdata = [list(map(hog,row)) for row in deskewed]
trainData = np.float32(hogdata).reshape(-1,64)
responses = np.repeat(np.arange(10),250)[:,np.newaxis]

opencv_tutorials/python/code_104/opencv_104.py

Lines 6 to 18 in d1dacf7

	def get_hog_descriptor(image):
	hog = cv.HOGDescriptor()
	h, w = image.shape[:2]
	rate = 64 / w
	image = cv.resize(image, (64, np.int(rate*h)))
	gray = cv.cvtColor(image, cv.COLOR_BGR2GRAY)
	bg = np.zeros((128, 64), dtype=np.uint8)
	bg[:,:] = 127
	h, w = gray.shape
	dy = (128 - h) // 2
	bg[dy:h+dy,:] = gray
	fv = hog.compute(bg, winStride=(8, 8), padding=(0, 0))
	return fv

这两个有什么区别吗？

是否可以提供对应cpp tutorials

如题

opencv4.0中sift问题

想知道楼主怎么解决的sift等算法在一些opencv高版本中无法使用的问题

Code012 lost file

The code012 lost the "test.avi" file