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GNU GENERAL PUBLIC LICENSE
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<https://www.gnu.org/licenses/why-not-lgpl.html>.

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# Cascade_tools
Automate the retraining of cascade classifiers with OpenCV by providing:
- a simple annotation tool for image/video capture and save
- generation of additional positive images from existing positives and negatives
- retraining of OpenCV Cascade classifiers using HAAR, LBP features
<tr>
<th>
<a name="tracker" href=""><img src="./info/qc.png" alt="400" width="400"></a>
</th>
</tr>
# Directory structure
```
.
├── annotate.py -> image/video annotation tool
├── clean.sh -> clean all files directory for a new training
├── data -> cascade classifier output directory
├── genpos -> directory will contain the additional positives generated from the existing ones
├── genpos.sh -> generate additional positives from the existing ones
├── info -> images displayed in the readme file
├── LICENSE
├── neg -> holds the negative images for training
├── pos -> holds the positive images for training
├── prepare_data.py -> generate the dat files needed for OpenCV cascade tool
├── raw -> directory to store annotated images
├── README.md
├── test_cascade.py -> simple tools to test cascade classifier
└── train.sh -> start the training process
```
# How to use
The scrips are relying on the built-in tools of OpenCV, therefore, to work OpenCV must be installed first installed.
### Step 1 - opencv install
```
sudo apt-get install python-opencv
```
Test existence of the OpenCV cascade trainer tools, type in the terminal window:
```
opencv_[TAB]
```
The following list should appear:
```
opencv_annotation opencv_traincascade opencv_visualisation
opencv_createsamples opencv_version
```
If we have the screen above, we are settled.
### Step 2 - Collect negative and positive images
Cascade is very sensitive to the training data, therefore by collecting the negative samples, makes sure that no object which follows to be detected is contained. Negatives can be collected from the internet, the format shall be *.jpg, size does not matter.
Collecting positive samples can happen using the annotation tool. Captures will be saved in the "raw" directory
```
python3 annotate.py --help
usage: annotate.py [-h] [-cam CAM] [-vid VID]
Simple annotation tool Use "a" to start to annotate, ENTER to return
optional arguments:
-h, --help show this help message and exit
-cam CAM Camera index
-vid VID Video file
```
Usually, there are needed about ~300 positive images and 1000-1200 negatives for reasonable training with less false positives.
### Step 3 - Retrain Cascade classifier
The train.sh script does the 'dirty work'. There are many hyperparameters to set, which can influence the classification accuracy but training time too.
```
# Number of training stages
nrStages=20
# detection types [HAAR,LBP]
fn=HAAR
# object size, width, height
imgw=30
imgh=21
```
Usually, a well-trained cascade will reach 20 stages of the training. HAAR is slower than LBP, but more accurate. The image size by default is (24,24), here is settled for rectangle objects. Changing these parameters will have a high impact on training time.
In case everything went well, the training will start...
```
===== TRAINING 0-stage =====
<BEGIN
POS count : consumed 13 : 13
NEG count : acceptanceRatio 13 : 1
Precalculation time: 0
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 1|
+----+---------+---------+
| 2| 1| 0|
+----+---------+---------+
END>
Training until now has taken 0 days 0 hours 0 minutes 0 seconds.
```
After the training has finished (i.e. max number of stages reached), a 'cascade.xml' fill will be created automatically in the 'data' folder.
### Step 4 - testing the results
After the 'cascade.xml' file was created, testing follows. Use the below provided utility for fast feedback.
```
python3 test_cascade.py --help
usage: test_cascade.py [-h] [-cam CAM] [-n N] [-s S]
Cascade tester. Defaults: -cam 0 -n 0 -s 1.1
optional arguments:
-h, --help show this help message and exit
-cam CAM Camera ID
-n N Number of neighbors for detections
-s S Scale factor
```
If there appears a lot of random detections (false positives):
<tr>
<th>
<a name="tracker" href=""><img src="./info/false_positives.png" alt="400" width="400"></a>
</th>
</tr>
- try to adjust the classifier hyperparameters in 'test_cascade.py' file
```
python3 test_cascade.py -n 2 -s 1.2
```
- increase the number of positive/negative samples, use 'clean.sh' to clear the intermediate files and retrain the cascade classifier till the results are acceptable.
# Resources
https://docs.opencv.org/2.4/doc/user_guide/ug_traincascade.html
/Enjoy.

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#! /usr/bin/python
from datetime import datetime
import cv2
import numpy as np
import argparse
import os
# input arguments
parser = argparse.ArgumentParser(description='Simple annotation tool \n \
Use "a" to start annotate, ENTER to return \n')
parser.add_argument('-cam', type=int, help='Camera index', default=1)
parser.add_argument('-vid', type=str, help='Video file', default='')
args = parser.parse_args()
path, filename = os.path.split(os.path.realpath(__file__))
# select video or camera
if args.vid != '':
cap = cv2.VideoCapture(args.vid)
else:
cap = cv2.VideoCapture(args.cam)
roi = []
frame = np.array((480,640,3), dtype=np.uint8)
img = np.array((120,160,3), dtype=np.uint8)
ccount = 0
cv2.namedWindow('annotate')
cv2.namedWindow('capture')
while(cap.isOpened()):
ret, frame = cap.read()
frame = cv2.resize(frame,(640,480))
k = cv2.waitKey(10)
# quit on ESC key
if k == 27:
cv2.destroyAllWindows()
exit()
#start annotation, save file with timestamp
if k== ord('a'):
roi = cv2.selectROI('annotate',frame)
img = frame[int(roi[1]):int(roi[1]+roi[3]),int(roi[0]):int(roi[0]+roi[2])]
now = datetime.now()
ts = datetime.timestamp(now)
cv2.imwrite(path +'/raw/'+ 'img_' + str(int(ts)) + '.jpg', img)
ccount +=1
print ("Capture count:" + str(ccount))
cv2.imshow('capture',img)
cv2.imshow('annotate',frame)
cap.close()
cv2.destroyAllWindows()

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#!/bin/bash
rm -rfv data/*
rm -rfv genpos/*
rm -f neg.txt
rm -f info.dat
rm -f positives.vec

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#!/bin/bash
echo "Generates positive pictures from existing positives and negatives"
echo "Parameters: postine width, hight and number of positives to generate"
# celanup
rm -rfv genpos/*
# script parameters
imgw=30
imgh=21
# number of positives forma a single image
nrPos=10
# generate positives for all images from ./genpos folder
FILES="./pos"
# create positive dat file and negative txt file
python3 ./prepare_data.py -posWidthX 80 -posWidthY 60 -negWidthX 160 -negWidthY 120
for f in $FILES/*
do
# create positives from a single image
opencv_createsamples -img $f -bg neg.txt -info ./genpos/genpos.dat -pngoutput genpos -maxxangle 1.1 -maxyangle 1.1 -maxzangle 1.1 -num $nrPos -w $imgw -h $imgh
done
#concatenuate the original positives with the generated ones
SCRIPTPATH="$( cd "$(dirname "$0")" >/dev/null 2>&1 ; pwd -P )"
FILEGP='./genpos/genpos.dat'
while read line; do
# reading each line, append the absolute path and update .dat file
str=$SCRIPTPATH/pos/$line
echo $str >> ./info.dat
done < $FILEGP
# copy all generated .jpg files to pos folder
cp ./genpos/*jpg ./pos

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import os
import glob
from PIL import Image
for folder in ['pos', 'neg']:
for f in glob.glob(folder + '/*.png'):
img = Image.open(f)
rgb_img = img.convert('RGB')
os.remove(f)

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#!/usr/bin/env python3
import cv2
import numpy as np
import sys
import os
import time
import glob
import argparse
# input arguments
parser = argparse.ArgumentParser(description='Prepar dataset for Cascade trainer.\n Defaults: posWidthX=50, posWidthY=50, negWidthX=100, negWidthY=100, location /pos/*.*, /neg/*.*')
parser.add_argument('-posWidthX', type=int, help='Positive sample x Width', default=80)
parser.add_argument('-posWidthY', type=int, help='Positive sample y Width', default=60)
parser.add_argument('-negWidthX', type=int, help='Negative sample x Width', default=160)
parser.add_argument('-negWidthY', type=int, help='Negative sample y Width', default=120)
parser.add_argument('-pos', type=str, help='Positive samples location', default='/pos/*.*')
parser.add_argument('-neg', type=str, help='Negative samples location', default='/neg/*.*')
args = parser.parse_args()
# sample sizes
POS_SIZE=(args.posWidthX,args.posWidthY)
NEG_SIZE = (args.negWidthX,args.negWidthY)
# current path
path, filename = os.path.split(os.path.realpath(__file__))
# read files
pfiles = glob.glob(path + args.pos)
nfiles = glob.glob(path + args.neg)
# check directory structure
if len(pfiles)== 0:
print ('no positive images found, check pos dir!')
exit
if len(nfiles)== 0:
print ('no negative images found, check neg dir!')
exit
# create positive images descriptor
f = open(path +'/info.dat','w')
for pf in pfiles:
# create tag to write in info.dat => objnr=1 startx=0 starty=0 endx=50 endy=50
infoline = pf + ' 1 0 0 ' + str(POS_SIZE[0]) + ' ' + str(POS_SIZE[1]) +'\n'
f.write(infoline)
# resize img to desired size
img = cv2.imread(pf)
h,w = img.shape[:2]
# resize if picture dimensiona are different
if (w,h) != POS_SIZE:
img = cv2.resize(img,POS_SIZE)
try:
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img = cv2.equalizeHist(img)
except:
pass
cv2.imwrite(pf,img)
f.close()
# create negative images descriptor
f = open(path +'/neg.txt','w')
for nf in nfiles:
# print(nf)
f.write(nf + '\n')
img = cv2.imread(nf)
h,w = img.shape[:2]
# resize if picture dimensiona are different
if (w,h) != NEG_SIZE:
img = cv2.resize(img,NEG_SIZE)
try:
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img = cv2.equalizeHist(img)
except:
pass
cv2.imwrite(nf,img)
f.close()

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#!/usr/bin/env python3
import cv2
import numpy as np
import os
import argparse
class Cascade():
def __init__(self,data_file):
"""[summary]
Args:
data_file (str): path to the cascade file
"""
self.cc = cv2.CascadeClassifier()
self.cc.load(data_file)
self.objects = []
self.scale = 1.1
self.neighbor = 0
def set_parameters(self, scale=1.1, neighbor=0):
"""
Set detection parameters
Args:
scale (float, optional): Scale factor. Defaults to 1.1.
neighbor (int, optional): Number of neighbors for detections. Defaults to 0.
"""
self.scale = scale
self.neighbor = neighbor
def get_detections(self, img):
"""
makes cascade detections
Args:
img (image): input - RGB image
returns a list of (x,y,w,h) for objects detected
"""
img_g = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
img_g = cv2.equalizeHist(img_g)
detections = self.cc.detectMultiScale(img_g,self.scale,self.neighbor, flags=cv2.CASCADE_DO_CANNY_PRUNING)
return detections
def display(self, img):
"""
display on an image the detections
Args:
img (image): color RGB image
"""
detections = self.get_detections(img)
for (x,y,w,h) in detections:
cv2.rectangle(img,(x,y),(x+w,y+h),[0,255,0],2)
return img
if __name__ == '__main__':
# input arguments
parser = argparse.ArgumentParser(description='Cascade tester.\n Defaults: -cam 0 -n 0 -s 1.1')
parser.add_argument('-cam', type=int, help='Camera ID', default=0)
parser.add_argument('-n', type=int, help='Number of neighbors for detections', default=0)
parser.add_argument('-s', type=float, help='Scale factor', default=1.1)
args = parser.parse_args()
path, filename = os.path.split(os.path.realpath(__file__))
f = path + '/data/cascade.xml'
cap = cv2.VideoCapture(args.cam)
cd = Cascade(f)
cd.set_parameters(args.s,args.n)
if not cap.isOpened:
exit(0)
while True:
ret, frame = cap.read()
if frame is None:
break
if cv2.waitKey(10) == 27:
break
frame = cd.display(frame)
cv2.imshow('Capture', frame)

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python png_to_jpg.py
echo "Create vector file from positives, negatives and starts the training"
echo "Parameters: posivies size, number of posties, "
# Number of training stages
nrStages=20
# detection types [HAAR,LBP]
# fn=HAAR
# object size, width, height
imgw=24
imgh=24
# number of images containg the object to detect (see pos directory content)
nrf=0
FILES=./pos/*.jpg
for f in $FILES; do
nrf=$((nrf+1))
done
nrPos=$nrf
nrPosThreeTimes=$(($nrPos * 3))
# number of images NOT containg the object to detect (see neg directory content)
nrf=0
FILES=./neg/*.jpg
for f in $FILES; do
nrf=$((nrf+1))
done
nrNeg=$nrf
# # create positive dat file and negative txt file
python3 ./prepare_data.py \
-posWidthX 24 \
-posWidthY 24 \
-negWidthX 64 \
-negWidthY 64
# create vector file from positives
opencv_createsamples \
-info info.dat \
-vec positives.vec \
-bg neg.txt \
-maxxangle 1.1 \
-maxyangle 1.1 \
-maxzangle 1.1 \
-w $imgw -h $imgh \
-num $nrPos
# create positives from a single image
#opencv_createsamples \
# -img object.jpg \
# -bg neg.txt \
# -info info/info.lst \
# -pngoutput info \
# -maxxangle 1.1 \
# -maxyangle 1.1 \
# -maxzangle 1.1 \
# -num $nrPos \
# -w $imgw -h $imgh
# train cascade
opencv_traincascade \
-data data \
-vec positives.vec \
-bg neg.txt \
-numPos $nrPos \
-numNeg $nrNeg \
-numStages $nrStages \
-w $imgw \
-h $imgh \
-minHitRate 0.999 \
-maxFalseAlarmRate 0.5 \
-mode ALL \
-numThreads 4 \
-precalcValBufSize 8192 -precalcIdxBufSize 8192
# -featureType $fn \

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vecs/
venv/
opencv/
samples/
annotations/
stage_outputs/
negative_images/
positive_images/
positive_images_old/
positive_images_gray/
.vscode/
.venv
*.MOV
*.vec
*.jpg
*.png
*.avi

207
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`createsamples.pl`: Copyright (c) 2008, Naotoshi Seo
From: https://github.com/sonots/tutorial-haartraining
`mergevec.py`: Copyright (c) 2014, Blake Wulfe
From: https://github.com/wulfebw/mergevec
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# Training, Classifying (Haar Cascades), and Tracking
Training our own Haar Cascade in OpenCV with Python. A cascade classifier has multiple stages of filtration. Using a sliding window approach, the image region within the window goes through the cascade. Can easily test accuracy of cascade with `classifier.py` script, which takes single images, directory of images, videos, and camera inputs. However, we want to also track our ROI (region of interest). This is because detection (through cascades, etc) is in general, more consuming and computationally taxing. Tracking algorithms are generally considered less taxing, because you know a lot about the apperance of the object in the ROI already. Thus, in the next frame, you use the information from the previous frame to predict and localize where the object is in the frames after.
There are many different types of tracking algorithms that are available through `opencv_contrib/tracking`, such as KCF, MOSSE, TLD, CSRT, etc. Here's a [good video](https://www.youtube.com/watch?v=61QjSz-oLr8) that demonstrates some of these tracking algorithms. Depending on your use case, the one you choose will differ.
## Jump to Section
* [Environment Setup](#environment-setup)
* [Image Scraping](#image-scraping)
* [Postive & Negative Image Sets](#positive-&-negative-image-sets)
* [Positive Samples Image Augmentation](#positive-samples-image-augmentation)
* [CSV Bounding Box Coordinates](#csv-bounding-box-coordinates)
* [Training](#training)
* [Testing Cascade](#testing-cascade)
* [Video Conversions](#video-conversions)
* [Contributing](#contributing)
* [Acknowledgements](#acknowledgements)
* [References](#references)
## Environment Setup
* Ubuntu 18.04; 20.04
* OpenCV 3.x.x (for running cascade training functions; built from source)
* OpenCV 4.4.0 (for running tracking algorithms)
* OpenCV Contrib (branch parallel with OpenCV 4.4.0)
As a clarification, the listed Ubuntu variants are for quick easy installs, depending on whether you're using `apt` or `pip`. To specifically get the OpenCV version you want, you will need to build from source (especially when you want to downgrade packages). I mainly used a Python virtual environment `venv` for package management. You can build and install OpenCV from source in the virtual environment (especially if you want a specific development branch or full control of compile options), or you can use `pip` locally in the `venv`. Packages included are shown in the `requirements.txt` file for reproducing the specific environment.
The project directory tree will look similar to the following below, and might change depending on the arguments passed to the scripts.
<blockquote>
```
.
├── classifier.py
├── bin
│   └── createsamples.pl
├── negative_images
│   └── *.jpg / *.png
├── positive_images
│   └── *.jpg / *.png
├── negatives.txt
├── positives.txt
├── requirements.txt
├── samples
│   └── *.vec
├── stage_outputs
│   ├── cascade.xml
│   ├── params.xml
│   └── stage*.xml
├── tools
│   ├── bbox_from_vid.py
│   └── mergevec.py
└── venv
```
</blockquote>
## Image Scraping
Go ahead and web scrape relevant negative images for training. Once you have a good amount, filter extensions that aren't `*.jpg` or `*.png` such as `*.gif`. Afterwards, we'll convert all the `*.png` images to `*jpg` using the following command:
```
mogrify -format jpg *.png
```
Then we can delete these `*.png` images. Let's also rename all the images within the directoy to be `img.jpg`
```
ls | cat -n | while read n f; do mv "$f" "img$n.jpg"; done
```
To check if all files within our directory are valid `*.jpg` files:
```
find -name '*.jpg' -exec identify -format "%f" {} \; 1>pass.txt 2>errors.txt
```
## Positive & Negative Image Sets
Positive images correspond to images with detected objects. Images were cropped to 150 x 150 px training set. Negative images are images that are visually close to positive images, but *must not have* any positive image sets within.
<blockquote>
```
/images
img1.png
img2.png
positives.txt
```
</blockquote>
To generate your `*.txt` file, run the following command, make sure to change image extension to whatever file type you're using.
```
find ./positive_images -iname "*.png" > positives.txt
```
As a quote from OpenCV docs:
<blockquote>
Negative samples are taken from arbitrary images. These images must not contain detected objects. [...] Described images may be of different sizes. But each image should be (but not nessesarily) larger then a training window size, because these images are used to subsample negative image to the training size.
</blockquote>
## Positive Samples Image Augmentation
We need to create a whole bunch of image samples, and we'll be using `OpenCV 3.x.x` to augment these images. [These tools / functionalities were disabled during legacy C API](https://github.com/opencv/opencv/issues/13231#issuecomment-440577461), so we'll need to first be on a downgraded version of OpenCV, and once we have our trained cascade model, we can upgrade back to `4.x.x`. As mentioned in the link earlier, most modern approaches use deep learning approaches. However having used Cascades, they still their applications! Anyways, to create a training set as a collection of PNG images:
```
opencv_createsamples -img ~/opencv-cascade-tracker/positive_images/img1.png\
-bg ~/opencv-cascade-tracker/negatives.txt -info ~/opencv-cascade-tracker/annotations/annotations.lst\
-pngoutput -maxxangle 0.1 -maxyangle 0.1 -maxzangle 0.1
```
But we need a whole bunch of these. To augment a set of positive samples with negative samples, let's run the perl script that Naotoshi Seo wrote:
```
perl bin/createsamples.pl positives.txt negatives.txt samples 1500\
"opencv_createsamples -bgcolor 0 -bgthresh 0 -maxxangle 1.1\
-maxyangle 1.1 maxzangle 0.5 -maxidev 40 -w 50 -h 50"
```
Merge all `*.vec` files into a single `samples.vec` file:
```
python ./tools/mergevec.py -v samples/ -o samples.vec
```
**Errors**: If you run into the following error when running `mergevec.py`:
```
Traceback (most recent call last):
File "./tools/mergevec.py", line 170, in <module>
merge_vec_files(vec_directory, output_filename)
File "./tools/mergevec.py", line 133, in merge_vec_files
val = struct.unpack('<iihh', content[:12])
struct.error: unpack requires a string argument of length 12
```
You need to remove all `*.vec` files with size 0 in your `samples` directory. Simply `cd samples` into the directory and double check with `ls -l -S` for file sizes, and run:
```
find . -type f -empty -delete
```
Note: others have said that using artifical data vectors is not the best way to train a classifier. Personally, I have used this method and it worked fine for my use cases. However, you may approach this idea with a grain of salt and skip this step. If you want to hand select your ROI, you may use OpenCV's `opencv_annotation` function to select your regions of interest in a directory of images.
If you don't want to use artifical data, you will need to have your images and the coordinates of your region of interest. You can then use `opencv_createsamples` to merge all these images into a single vector `.vec` file and use this for training instead. You will need to supply an `info.dat` file that contains the image instance and the object coordinates in `(x, y, width, height)`. Your `info.dat` should look something like below, and can also be created using `opencv_annotation` or other customized data piplining methodologies you will need to implement.
<blockquote>
```
img/img1.jpg 1 140 100 45 45
img/img2.jpg 2 100 200 50 50 50 30 25 25
```
</blockquote>
## CSV Bounding Box Coordinates
In `tools`, there is a script called `bbox_from_vid.py` which can be used to generate the `(x_min, y_min, x_max, y_max)` coordinates, where `min` is from top left point of the bounding box and `max` is the bottom right point. It uses OpenCV's tracking algorithms to track the object selected.
```
usage: bbox_from_vid.py [-h] [-v] [-o] [-c] [-z]
Get bbox / ROI coords and training images from videos
optional arguments:
-h, --help show this help message and exit
-v, --vid specify video to be loaded
-o, --center select bounding box / ROI from center point
-c, --csv export CSV file with bbox coords
-z, --scale decrease video scale by scale factor
```
For example, if you wanted to save the bounding box coordinates of a tracked object:
```
./bbox_from_vid.py -v video_input.avi -c vid_bbox_coords.csv
```
## Training
There are two ways in OpenCV to train cascade classifier.
* `opencv_haartraining`
* `opencv_traincascade` - Newer version. Supports both Haar and LBP (Local Binary Patterns)
These were the parameters I used for my initial cascade training. Later, we can introduce a larger dataset. To begin training using `opencv_traincascade`:
```
opencv_traincascade -data stage_outputs -vec samples.vec -bg negatives.txt\
-numStages 20 -minHitRate 0.999 -maxFalseAlarmRate 0.5 -numPos 390\
-numNeg 600 -w 50 -h 50 -mode ALL -precalcValBufSize 8192\
-precalcIdxBufSize 8192
```
The first cascade worked relatively well. However, performance suffered in different lighting conditions. As a result, I trained a second cascade with a larger dataset that included different lighting conditions..
```
opencv_traincascade -data stage_outputs -vec samples.vec -bg negatives.txt\
-numStages 22 -minHitRate 0.993 -maxFalseAlarmRate 0.5 -numPos 1960\
-numNeg 1000 -w 50 -h 50 -mode ALL -precalcValBufSize 16384\
-precalcIdxBufSize 16384
```
Parameters for tuning `opencv_traincascade` are available in the [documentation](https://docs.opencv.org/4.4.0/dc/d88/tutorial_traincascade.html). `precalcValBufSize` and `precalcIdxBufSize` are buffer sizes. Currently set to 8192 Mb. If you have available memory, tune this parameter as training will be faster.
Something important to note is that
> vec-file has to contain `>= [numPos + (numStages - 1) * (1 - minHitRate) * numPos] + S`, where `S` is a count of samples from vec-file that can be recognized as background right away
`numPos` and `numNeg` are the number of positive and negative samples we use in training for every classifier stage. Therefore, `numPos` should be relatively less than our total number of positive samples, taking into consideration the number of stages we'll be running.
```
===== TRAINING 0-stage =====
<BEGIN
POS count : consumed 1960 : 1960
NEG count : acceptanceRatio 1000 : 1
Precalculation time: 106
+----+---------+---------+
| N | HR | FA |
+----+---------+---------+
| 1| 1| 1|
+----+---------+---------+
| 2| 1| 1|
+----+---------+---------+
| 3| 1| 1|
+----+---------+---------+
| 4| 1| 0.568|
+----+---------+---------+
| 5| 0.99949| 0.211|
+----+---------+---------+
END>
Training until now has taken 0 days 1 hours 3 minutes 47 seconds.
```
Each row of the training output for each stage represents a feature that's being trained. HR stands for Hit Ratio and FA stands for False Alarm. Note that if a training stage only has a few features (e.g. N = 1 ~ 3), that can suggest that the training data you used was not optimized.
**Note**: You can always pause / stop your cascade training and just build your final `cascade.xml` with the training stages that you've completed thus far. Just run your `opencv_traincascade` script and change the `-numStages` argument up to whichever completed stage you want, while *keeping every other parameter the same*. Your `cascade.xml` will be created.
```
opencv_traincascade -data stage_outputs -vec samples.vec -bg negatives.txt -numStages 22 -minHitRate 0.9993 -maxFalseAlarmRate 0.5 -numPos 1960 -numNeg 1000 -w 50 -h 50 -mode ALL -precalcValBufSize 16384 -precalcIdxBufSize 16384
```
## Testing Cascade
To test how well our cascade performs, run the `classifier.py` script.
```
usage: classifier.py [-h] [-s] [-c] [-i] [-d] [-v] [-w] [-f] [-o] [-z] [-t]
Cascade Classifier
optional arguments:
-h, --help show this help message and exit
-s, --save specify output name
-c, --cas specify specific trained cascade
-i, --img specify image to be classified
-d, --dir specify directory of images to be classified
-v, --vid specify video to be classified
-w, --cam enable camera access for classification
-f, --fps enable frames text (TODO)
-o, --circle enable circle detection
-z, --scale decrease video scale by scale factor
-t, --track select tracking algorithm [KCF, CSRT, MEDIANFLOW]
```
When testing a tracking algorithm, **pass the scale parameter**. For example, to run a video through the classifier and save the output:
```
./classifier.py -v ~/video_input.MOV -s ~/video_output -z 2 -t KCF
```
## Video Conversions
The `classifier.py` automatically saves output videos as `*.avi` (fourcc: XVID). If you need other video types, this can be done very easily with `ffmpeg`. There are way more command arguments, especially if you want to consider encoding and compression types. The following command below converts the `*.avi` to `*.mp4` and compresses it.
```
ffmpeg -i video_input.avi -vcodec libx264 -crf 30 video_output.mp4
```
## Contributing
Pull requests are welcomed.
## Acknowledgements
For releasing their tools and notes under MIT license.
* [Naotoshi Seo](https://github.com/sonots) - `createsamples.pl`
* [Blake Wulfe](https://github.com/wulfebw) - `mergevec.py`
* [Thorsten Ball](https://github.com/mrnugget)
## References
* [OpenCV - Cascade Classifier Training](https://docs.opencv.org/master/dc/d88/tutorial_traincascade.html)
* [OpenCV - Face Detection using Haar Cascades](https://docs.opencv.org/master/d2/d99/tutorial_js_face_detection.html)
* [Naotoshi Seo - Tutorial: OpenCV haartraining](http://note.sonots.com/SciSoftware/haartraining.html)
* [Thorsten Ball - Train your own OpenCV Haar Classifier](https://coding-robin.de/2013/07/22/train-your-own-opencv-haar-classifier.html)

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#!/usr/bin/perl
use File::Basename;
use strict;
##########################################################################
# Create samples from an image applying distortions repeatedly
# (create many many samples from many images applying distortions)
#
# perl createtrainsamples.pl <positives.dat> <negatives.dat> <vec_output_dir>
# [<totalnum = 7000>] [<createsample_command_options = ./createsamples -w 20 -h 20...>]
# ex) perl createtrainsamples.pl positives.dat negatives.dat samples
#
# Author: Naotoshi Seo
# Date : 09/12/2008 Add <totalnum> and <createsample_command_options> options
# Date : 06/02/2007
# Date : 03/12/2006
#########################################################################
my $cmd = './createsamples -bgcolor 0 -bgthresh 0 -maxxangle 1.1 -maxyangle 1.1 maxzangle 0.5 -maxidev 40 -w 20 -h 20';
my $totalnum = 7000;
my $tmpfile = 'tmp';
if ($#ARGV < 2) {
print "Usage: perl createtrainsamples.pl\n";
print " <positives_collection_filename>\n";
print " <negatives_collection_filename>\n";
print " <output_dirname>\n";
print " [<totalnum = " . $totalnum . ">]\n";
print " [<createsample_command_options = '" . $cmd . "'>]\n";
exit;
}
my $positive = $ARGV[0];
my $negative = $ARGV[1];
my $outputdir = $ARGV[2];
$totalnum = $ARGV[3] if ($#ARGV > 2);
$cmd = $ARGV[4] if ($#ARGV > 3);
open(POSITIVE, "< $positive");
my @positives = <POSITIVE>;
close(POSITIVE);
open(NEGATIVE, "< $negative");
my @negatives = <NEGATIVE>;
close(NEGATIVE);
# number of generated images from one image so that total will be $totalnum
my $numfloor = int($totalnum / $#positives);
my $numremain = $totalnum - $numfloor * $#positives;
# Get the directory name of positives
my $first = $positives[0];
my $last = $positives[$#positives];
while ($first ne $last) {
$first = dirname($first);
$last = dirname($last);
if ( $first eq "" ) { last; }
}
my $imgdir = $first;
my $imgdirlen = length($first);
for (my $k = 0; $k < $#positives; $k++ ) {
my $img = $positives[$k];
my $num = ($k < $numremain) ? $numfloor + 1 : $numfloor;
# Pick up negative images randomly
my @localnegatives = ();
for (my $i = 0; $i < $num; $i++) {
my $ind = int(rand($#negatives));
push(@localnegatives, $negatives[$ind]);
}
open(TMP, "> $tmpfile");
print TMP @localnegatives;
close(TMP);
#system("cat $tmpfile");
!chomp($img);
my $vec = $outputdir . substr($img, $imgdirlen) . ".vec" ;
print "$cmd -img $img -bg $tmpfile -vec $vec -num $num" . "\n";
system("$cmd -img $img -bg $tmpfile -vec $vec -num $num");
}
unlink($tmpfile);

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#!/usr/bin/env python3
#import matplotlib.pyplot as plt
import argparse as ap
import numpy as np
import cv2 as cv
import os
import sys
class CustomFormatter(ap.HelpFormatter):
def _format_action_invocation(self, action):
if not action.option_strings:
metavar, = self._metavar_formatter(action, action.dest)(1)
return metavar
else:
parts = []
if action.nargs == 0:
parts.extend(action.option_strings)
else:
default = action.dest.upper()
args_string = self._format_args(action, default)
for option_string in action.option_strings:
#parts.append('%s %s' % (option_string, args_string))
parts.append('%s' % option_string)
parts[-1] += ' %s'%args_string
return ', '.join(parts)
# Parser Arguments
parser = ap.ArgumentParser(description='Cascade Classifier', formatter_class=CustomFormatter)
parser.add_argument("-s", "--save", metavar='', help="specify output name")
parser.add_argument("-c", "--cas", metavar='', help="specify specific trained cascade", default="./stage_outputs/cascade.xml")
parser.add_argument("-i", "--img", metavar='', help="specify image to be classified")
parser.add_argument("-d", "--dir", metavar='', help="specify directory of images to be classified")
parser.add_argument("-v", "--vid", metavar='', help="specify video to be classified")
parser.add_argument("-w", "--cam", metavar='', help="enable camera access for classification")
parser.add_argument("-f", "--fps", help="enable frames text (TODO)", action="store_true")
parser.add_argument("-o", "--circle", help="enable circle detection", action="store_true")
parser.add_argument("-z", "--scale", metavar='', help="decrease video scale by scale factor", type=int, default=1)
parser.add_argument("-t", "--track", metavar='', help="select tracking algorithm [KCF, CSRT, MEDIANFLOW]", choices=['KCF', 'CSRT', 'MEDIANFLOW'])
args = parser.parse_args(sys.argv[1:])
# Load the trained cascade
cascade = cv.CascadeClassifier()
if not cascade.load(args.cas):
print("Can't find cascade file. Do you have the directory ./stage_outputs/cascade.xml")
exit(0)
def plot():
pass
def detect_circles(src):
img = src
img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
img_blur = cv.medianBlur(img_gray, 5)
rows = img_blur.shape[0]
#Images circles = cv.HoughCircles(img_blur, cv.HOUGH_GRADIENT, 1, rows / 3, param1=100, param2=40, maxRadius=40)
circles = cv.HoughCircles(img_blur, cv.HOUGH_GRADIENT, 1, rows/3, param1=100, param2=15, minRadius=10, maxRadius=15)
if circles is not None:
circles = np.uint16(np.around(circles))
for i in circles[0, :]:
center = (i[0], i[1])
# circle center
cv.circle(img, center, 1, (0, 100, 100), 3)
# circle outline
radius = i[2]
cv.circle(img, center, radius, (255, 0, 255), 3)
return img
def choose_tracker():
OPENCV_TRACKERS = {
'KCF': cv.TrackerKCF_create(),
'CSRT': cv.TrackerCSRT_create(),
'MEDIANFLOW': cv.TrackerMedianFlow_create()
}
tracker = OPENCV_TRACKERS[args.track]
return tracker
def tracking(vid, tracker):
ok, frame = vid.read()
frame = scale(frame, args.scale)
ok, roi = tracker.update(frame)
if ok:
p1 = (int(roi[0]), int(roi[1]))
p2 = (int(roi[0] + roi[2]), int(roi[1] + roi[3]))
cv.rectangle(frame, p1, p2, (0,255,0), 2, 1)
cpoint_circle = cv.circle(frame, (int(roi[0]+(roi[2]/2)), int(roi[1]+(roi[3]/2))), 3, (0,255,0), 3)
return frame
def save(frame):
# Need dimensions of frame to determine proper video output
fourcc = cv.VideoWriter_fourcc(*'XVID')
height, width, channels = frame.shape
out = cv.VideoWriter(args.save + '.avi', fourcc, 30.0, (width, height))
return out
def get_roi(frame):
# Get initial bounding box by running cascade detection on first frame
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
frame_gray = cv.GaussianBlur(frame_gray, (3, 3), 0)
cas_object = cascade.detectMultiScale(frame_gray)
if len(cas_object) == 0:
return []
roi = (cas_object[0][0], cas_object[0][1], cas_object[0][2], cas_object[0][3])
return roi
def get_cascade(frame):
frame_gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)
frame_gray = cv.GaussianBlur(frame_gray, (3, 3), 0)
cas_object = cascade.detectMultiScale(frame_gray)
for (x, y, w, h) in cas_object:
cv.rectangle(frame, (x,y), (x+w, y+h), (0,0,255), 2)
cpoint_circle = cv.circle(frame, (int(x+(w/2)), int(y+(h/2))), 3, (0,0,255), 3)
return frame
def scale(frame, scale_factor):
height, width, channels = frame.shape
scaled_height = int(height/scale_factor)
scaled_width = int(width/scale_factor)
resized_frame = cv.resize(frame, (scaled_width, scaled_height))
return resized_frame
def img_classifier():
# Read image, convert to gray, equalize histogram, and detect.
img = cv.imread(args.img, cv.IMREAD_COLOR)
img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
#img_gray = cv.equalizeHist(img_gray)
cas_object = cascade.detectMultiScale(img_gray)
for (x, y, w, h) in cas_object:
roi = cv.rectangle(img, (x,y), (x+w, y+h), (0,0,255), 2)
cpoint_circle = cv.circle(img, (int(x+(w/2)), int(y+(h/2))), 3, (0,0,255), 3)
if args.circle is True:
roi = img[y:y+h, x:x+w]
img = detect_circles(roi)
cv.imshow('image', img)
cv.waitKey(0)
cv.destroyAllWindows()
def dir_classifier():
imgs = []
for filename in os.listdir(args.dir):
img = cv.imread(os.path.join(args.dir, filename))
if img is not None:
img_gray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)
cas_object = cascade.detectMultiScale(img_gray)
for (x, y, w, h) in cas_object:
cv.rectangle(img, (x,y), (x+w, y+h), (0,0,255), 2)
cpoint_circle = cv.circle(img, (int(x+(w/2)), int(y+(h/2))), 3, (0,0,255), 3)
if args.circle is True:
roi = img[y:y+h, x:x+w]
img = detect_circles(roi)
cv.imshow(str(filename), img)
cv.waitKey(0)
imgs.append(img)
#print(imgs)
#return imgs
def vid_classifier():
vid = cv.VideoCapture(args.vid)
if not vid.isOpened():
print("Could not open video")
sys.exit()
# Read the first frame
_ , frame = vid.read()
frame = scale(frame, args.scale)
if not _:
print("Cannot read video file")
sys.exit()
if args.save is not None and _ is True:
out = save(frame=frame)
if args.track is not None and _ is True:
cas_roi = get_roi(frame)
while not cas_roi:
_, frame = vid.read()
frame = scale(frame, args.scale)
cas_roi = get_roi(frame)
tracker = choose_tracker()
tracker.init(frame, cas_roi)
while(vid.isOpened()):
_ , frame = vid.read()
frame = scale(frame, args.scale)
frame = get_cascade(frame)
if args.track is not None:
frame = tracking(vid=vid, tracker=tracker)
if args.circle is True:
roi = get_roi(frame)
roi_circle = frame[int(roi[1]):int(roi[1] + roi[3]), int(roi[0]):int(roi[0] + roi[2])]
frame = detect_circles(roi_circle)
cv.imshow('video', frame)
if args.save is not None:
out.write(frame)
if cv.waitKey(1) & 0xFF == ord('q'):
break
if args.save is not None:
out.release()
vid.release()
cv.destroyAllWindows()
def cam_classifier():
cam = cv.VideoCapture(0)
if not cam.isOpened():
raise IOError("Cannot access camera")
while(cam.isOpened()):
_, frame = cap.read()
frame = get_cascade(frame)
cv2.imshow('camera', frame)
if cv.waitKey(10) & 0xFF == ord('q'):
break
cam.release()
cv.destroyAllWindows()
if __name__ == "__main__":
if args.img is not None:
img_classifier()
elif args.vid is not None:
vid_classifier()
elif args.dir is not None:
dir_classifier()
elif args.cam is not None:
cam_clasifier()
else:
parser.print_help()

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cycler==0.10.0
kiwisolver==1.3.1
matplotlib==3.3.4
numpy==1.20.0
opencv-contrib-python==4.4.0.44
Pillow==8.1.0
pip-autoremove==0.9.1
pkg-resources==0.0.0
pyparsing==2.4.7
python-dateutil==2.8.1
six==1.15.0

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#!/usr/bin/env bash
set -ex
opencv_createsamples \
-img ./positive_images/img1.png\
-bg ./negatives.txt \
-info ./annotations/annotations.lst\
-pngoutput \
-maxxangle 0.1 \
-maxyangle 0.1 \
-maxzangle 0.1

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#!/usr/bin/env python3
import argparse as ap
import numpy as np
import cv2 as cv
import sys
class CustomFormatter(ap.HelpFormatter):
def _format_action_invocation(self, action):
if not action.option_strings:
metavar, = self._metavar_formatter(action, action.dest)(1)
return metavar
else:
parts = []
if action.nargs == 0:
parts.extend(action.option_strings)
else:
default = action.dest.upper()
args_string = self._format_args(action, default)
for option_string in action.option_strings:
#parts.append('%s %s' % (option_string, args_string))
parts.append('%s' % option_string)
parts[-1] += ' %s'%args_string
return ', '.join(parts)
# Parser Arguments
parser = ap.ArgumentParser(description='Get bbox / ROI coords and training images from videos', formatter_class=CustomFormatter)
parser.add_argument("-v", "--vid", metavar='', help="specify video to be loaded")
parser.add_argument("-o", "--center", help="select bounding box / ROI from center point", action='store_true')
parser.add_argument("-c", "--csv", metavar='', help="export CSV file with bbox coords")
parser.add_argument("-z", "--scale", metavar='', help="decrease video scale by scale factor", type=int, default=1)
args = parser.parse_args(sys.argv[1:])
class tracker_types:
CSRT = cv.TrackerCSRT_create()
KCF = cv.TrackerKCF_create()
MEDIANFLOW = cv.TrackerMedianFlow_create()
def __init__(self):
pass
def scale(frame, scale_factor):
height, width, channels = frame.shape
scaled_height = int(height/scale_factor)
scaled_width = int(width/scale_factor)
resized_frame = cv.resize(frame, (scaled_width, scaled_height))
return resized_frame
def create_csv(values):
np.savetxt(args.csv, values, delimiter=',', fmt='%s')
if __name__ == '__main__':
if args.vid is not None:
vid = cv.VideoCapture(args.vid)
if not vid.isOpened():
print("Could not open video")
sys.exit()
_, frame = vid.read()
frame = scale(frame, args.scale)
if not _:
print("Cannot read video file")
sys.exit()
bbox = cv.selectROI(frame, showCrosshair=True, fromCenter=args.center)
csv_values = np.array([["x_min", "y_min", "x_max", "y_max", "frame_num"]])
tracker = tracker_types.CSRT
tracker.init(frame, bbox)
while True:
_, frame = vid.read()
frame = scale(frame, args.scale)
frame_number = vid.get(cv.CAP_PROP_POS_FRAMES)
_, roi = tracker.update(frame)
if _:
p1 = (int(roi[0]), int(roi[1]))
p2 = (int(roi[0] + roi[2]), int(roi[1] + roi[3]))
cv.rectangle(frame, p1, p2, (0,255,0), 2, 1)
cpoint_circle = cv.circle(frame, (int(roi[0]+(roi[2]/2)), int(roi[1]+(roi[3]/2))), 3, (0,255,0), 3)
csv_data = np.array([[int(roi[0]), int(roi[1]), int(roi[0] + roi[2]), int(roi[1] + roi[3]), int(frame_number)]])
# If your object is stationary, and you just want to train different lighting conditions
# csv_data = np.array([[441, 328, 612, 482, int(frame_number)]])
csv_values = np.append(csv_values, csv_data, 0)
create_csv(csv_values)
else:
# Tracking failure
cv.putText(frame, "Tracking Failure", (100,80), cv.FONT_HERSHEY_SIMPLEX, 0.75,(0,0,255),2)
# Display result
cv.imshow("Video", frame)
# Quit with "Q"
if cv.waitKey(1) & 0xFF == ord('q'):
break
else:
parser.print_help()

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###############################################################################
# Copyright (c) 2014, Blake Wulfe
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
###############################################################################
"""
File: mergevec.py
Author: blake.w.wulfe@gmail.com
Date: 6/13/2014
File Description:
This file contains a function that merges .vec files called "merge_vec_files".
I made it as a replacement for mergevec.cpp (created by Naotoshi Seo.
See: http://note.sonots.com/SciSoftware/haartraining/mergevec.cpp.html)
in order to avoid recompiling openCV with mergevec.cpp.
To use the function:
(1) Place all .vec files to be merged in a single directory (vec_directory).
(2) Navigate to this file in your CLI (terminal or cmd) and type "python mergevec.py -v your_vec_directory -o your_output_filename".
The first argument (-v) is the name of the directory containing the .vec files
The second argument (-o) is the name of the output file
To test the output of the function:
(1) Install openCV.
(2) Navigate to the output file in your CLI (terminal or cmd).
(2) Type "opencv_createsamples -w img_width -h img_height -vec output_filename".
This should show the .vec files in sequence.
"""
import sys
import glob
import struct
import argparse
import traceback
def exception_response(e):
exc_type, exc_value, exc_traceback = sys.exc_info()
lines = traceback.format_exception(exc_type, exc_value, exc_traceback)
for line in lines:
print(line)
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('-v', dest='vec_directory')
parser.add_argument('-o', dest='output_filename')
args = parser.parse_args()
return (args.vec_directory, args.output_filename)
def merge_vec_files(vec_directory, output_vec_file):
"""
Iterates throught the .vec files in a directory and combines them.
(1) Iterates through files getting a count of the total images in the .vec files
(2) checks that the image sizes in all files are the same
The format of a .vec file is:
4 bytes denoting number of total images (int)
4 bytes denoting size of images (int)
2 bytes denoting min value (short)
2 bytes denoting max value (short)
ex: 6400 0000 4605 0000 0000 0000
hex 6400 0000 4605 0000 0000 0000
# images size of h * w min max
dec 100 1350 0 0
:type vec_directory: string
:param vec_directory: Name of the directory containing .vec files to be combined.
Do not end with slash. Ex: '/Users/username/Documents/vec_files'
:type output_vec_file: string
:param output_vec_file: Name of aggregate .vec file for output.
Ex: '/Users/username/Documents/aggregate_vec_file.vec'
"""
# Check that the .vec directory does not end in '/' and if it does, remove it.
if vec_directory.endswith('/'):
vec_directory = vec_directory[:-1]
# Get .vec files
files = glob.glob('{0}/*.vec'.format(vec_directory))
# Check to make sure there are .vec files in the directory
if len(files) <= 0:
print('Vec files to be merged could not be found from directory: {0}'.format(vec_directory))
sys.exit(1)
# Check to make sure there are more than one .vec files
if len(files) == 1:
print('Only 1 vec file was found in directory: {0}. Cannot merge a single file.'.format(vec_directory))
sys.exit(1)
# Get the value for the first image size
prev_image_size = 0
try:
with open(files[0], 'rb') as vecfile:
content = ''.join(str(line) for line in vecfile.readlines())
val = struct.unpack('<iihh', content[:12])
prev_image_size = val[1]
except IOError as e:
print('An IO error occured while processing the file: {0}'.format(f))
exception_response(e)
# Get the total number of images
total_num_images = 0
for f in files:
try:
with open(f, 'rb') as vecfile:
content = ''.join(str(line) for line in vecfile.readlines())
val = struct.unpack('<iihh', content[:12])
num_images = val[0]
image_size = val[1]
if image_size != prev_image_size:
err_msg = """The image sizes in the .vec files differ. These values must be the same. \n The image size of file {0}: {1}\n
The image size of previous files: {0}""".format(f, image_size, prev_image_size)
sys.exit(err_msg)
total_num_images += num_images
except IOError as e:
print('An IO error occured while processing the file: {0}'.format(f))
exception_response(e)
# Iterate through the .vec files, writing their data (not the header) to the output file
# '<iihh' means 'little endian, int, int, short, short'
header = struct.pack('<iihh', total_num_images, image_size, 0, 0)
try:
with open(output_vec_file, 'wb') as outputfile:
outputfile.write(header)
for f in files:
with open(f, 'rb') as vecfile:
content = ''.join(str(line) for line in vecfile.readlines())
data = content[12:]
outputfile.write(data)
except Exception as e:
exception_response(e)
if __name__ == '__main__':
vec_directory, output_filename = get_args()
if not vec_directory:
sys.exit('mergvec requires a directory of vec files. Call mergevec.py with -v /your_vec_directory')
if not output_filename:
sys.exit('mergevec requires an output filename. Call mergevec.py with -o your_output_filename')
merge_vec_files(vec_directory, output_filename)

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# NOTES
faulty made by colored version of photos

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import cv2
import numpy as np
# Load the image
img = cv2.imread("beach.jpg")
# Convert the image to grayscale
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
# Detect corners using the Harris corner detector
corners = cv2.goodFeaturesToTrack(gray, 100, 0.3, 10)
# Calculate the feature vectors using the SIFT descriptor
sift = cv2.xfeatures2d.SIFT_create()
kp, des = sift.detectAndCompute(gray, None)
# Match the feature vectors using the FLANN matcher
matcher = cv2.DescriptorMatcher_create(cv2.DESCRIPTOR_MATCHER_FLANNBASED)
matches = matcher.knnMatch(des, des, k=2)
# Draw the matched keypoints
cv2.drawMatches(img, kp, img, kp, matches, None, flags=cv2.DrawMatchesFlags_NOT_DRAW_SINGLE_POINTS)
# Display the image
cv2.imshow("Image", img)
cv2.waitKey(0)
cv2.destroyAllWindows()

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### youtubes
https://github.com/amannirala13/HAAR-Cascade-Trainer-Linux
https://www.youtube.com/watch?v=bsWVjt_QUhA
https://www.youtube.com/watch?v=fgx5LDOx4JY
C:\Users\logic\Downloads\anas_haartrain\02_haarTraining.bat