https://github.com/rajputrockstar/kazier
This is Modules aims to help you with your opencv Project to make it easier for your to focus on Project
https://github.com/rajputrockstar/kazier
ai computervision mediapipe opencv opencv-python pypi pypi-package python pythonprojects
Last synced: 9 months ago
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This is Modules aims to help you with your opencv Project to make it easier for your to focus on Project
- Host: GitHub
- URL: https://github.com/rajputrockstar/kazier
- Owner: RAJPUTRoCkStAr
- License: mit
- Created: 2024-07-30T05:16:44.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-08-06T19:07:00.000Z (over 1 year ago)
- Last Synced: 2025-03-09T13:22:05.782Z (10 months ago)
- Topics: ai, computervision, mediapipe, opencv, opencv-python, pypi, pypi-package, python, pythonprojects
- Language: Python
- Homepage:
- Size: 27.3 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Kaizer Package
Kaizer is a comprehensive package for face detection, hand tracking, pose estimation, and more using MediaPipe. It is designed to simplify your project development.
## Features
- **Face Detection**: Efficient and accurate face detection.
- **Hand Tracking**: Real-time hand tracking and gesture recognition.
- **Pose Estimation**: Full-body pose estimation.
- **FPS Calculation**: Measure frames per second for performance evaluation.
- **Utilities**: Additional tools to streamline your project work.
## Installation
You can install the package using pip:
```bash
pip install kaizer
```
## Usage
### Using Face Detection
```bash
from KAZIER import FaceDetector
import cv2
cap = cv2.VideoCapture(0)
detector = FaceDetector()
while True:
success, img = cap.read()
if not success:
break
img, bboxs = detector.find_faces(img)
if bboxs:
for _, bbox, _ in bboxs:
img = detector.imp_draw(img, bbox)
print("Bounding boxes:", bboxs)
img, faces = detector.find_face_mesh(img)
if faces:
print(f"Number of faces detected: {len(faces)}")
p1 = faces[0][33] # Example: left eye landmark
p2 = faces[0][263] # Example: right eye landmark
length, info, img = detector.findDistance(p1, p2, img)
print(f"Distance between points: {length}, Info: {info}")
cv2.imshow('Image', img)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
### Using fps
```bash
from KAZIER import FPS
import cv2
fps_counter = FPS()
cap = cv2.VideoCapture(0)
while True:
ret, frame = cap.read()
if not ret:
break
fps = fps_counter.showfps(frame, writetext=True, text_pos=(10, 50),
fthickness=2,tcolor=(0,255,250),
Fstyle=cv2.FONT_HERSHEY_DUPLEX,fscale=2,)
cv2.imshow('Frame', frame)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
### Using HAND DETECTION
```bash
from KAZIER import HandStar
import cv2
cap = cv2.VideoCapture(0)
detector = HandStar(maxHands=2)
while True:
success, img = cap.read()
if not success:
break
img = detector.detect_hands(img)
lmList = detector.get_hand_positions(img)
if len(lmList) != 0:
fingersList = detector.get_fingers_status()
for i, fingers in enumerate(fingersList):
length, img, lineInfo = detector.calculate_distance(4, 8, img, handNo=i)
cv2.imshow('Image', img)
if cv2.waitKey(1) == ord('q'):
break
cap.release()
cv2.destroyAllWindows()
```
### Using Pose Module
```bash
from KAZIER import PoseDetector
import cv2
cap = cv2.VideoCapture(0)
detector = PoseDetector()
while True:
success, img = cap.read()
img = cv2.resize(img, (680, 680))
img = detector.findPose(img)
lmList = detector.findPosition(img)
if lmList:
cv2.circle(img, (lmList[14][1], lmList[14][2]), 10, (250, 0, 0), cv2.FILLED)
length, img, info = detector.findDistance(lmList[11][1:3], lmList[15][1:3], img=img, color=(255, 0, 0), scale=10)
cv2.imshow("image", img)
if cv2.waitKey(1) == ord('q'):
break
```
### Using Utils
```bash
from KAZIER import Helper
import cv2
# Initialize the Helper class
utils = Helper()
# Download the image from URL
image_url = 'https://image.shutterstock.com/image-vector/dotted-spiral-vortex-royaltyfree-images-600w-2227567913.jpg'
image = utils.download_image_from_url(image_url)
# Make the background black
black_background_image = utils.make_background_black(image)
cv2.imshow('Black Background Image', black_background_image)
# Rotate the image by 45 degrees
rotated_image = utils.rotate_image(image, 45)
cv2.imshow('Rotated Image', rotated_image)
# Read another image from local filesystem
img2 = cv2.imread('med/ig.jpg')
# Stack images horizontally and vertically
hstacked_image = utils.hstack_images(image, img2)
cv2.imshow('Horizontally Stacked Image', hstacked_image)
vstacked_image = utils.vstack_images(image, img2)
cv2.imshow('Vertically Stacked Image', vstacked_image)
# Detect the color green in the image
detected_color = utils.detect_color(image, 'green')
cv2.imshow('Detected Color Image', detected_color)
# Detect corners in the image
image_with_corners = utils.detect_corners(image)
cv2.imshow('Image with Corners', image_with_corners)
# Add text to the image
image_with_text_left = utils.add_text(image, 'Hello World', (50, 50), font_name='hershey_triplex', color_name='blue', align='left')
cv2.imshow('Image with Text', image_with_text_left)
# Wait for a key press and then close all OpenCV windows
cv2.waitKey(0)
cv2.destroyAllWindows()
```
## License
- This project is licensed under the MIT License. See the LICENSE file for details.
## Contributing
- Contributions are welcome! Please open an issue or submit a pull request.