cvfiq 0.3

Computer Vision Helping Library/Functions

cvfiq

A Python Computer Vision library that makes it easy to run image processing and AI functions using OpenCV and MediaPipe.

Requirements

Library	Min	Max
Python	3.8	3.12
opencv-python	4.7.0	4.13.0.92
mediapipe	0.10.0	0.10.32
numpy	1.21.0	2.4.2
pyserial	3.0	3.5

Installation

pip install cvfiq

For ClassificationModule (optional, choose one):

pip install cvfiq[keras]        # standalone Keras
pip install cvfiq[tensorflow]   # TensorFlow

---

Modules

Face Detection

from cvfiq.FaceDetectionModule import FaceDetector
import cv2

cap = cv2.VideoCapture(0)
detector = FaceDetector()

while True:
    success, img = cap.read()
    img, bboxs = detector.findFaces(img)

    if bboxs:
        # bboxInfo keys: "id", "bbox", "score", "center", "keypoints"
        center = bboxs[0]["center"]
        keypoints = bboxs[0]["keypoints"]  # rightEye, leftEye, nose, mouth, rightEar, leftEar
        cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)

    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Hand Tracking

from cvfiq.HandTrackingModule import HandDetector
import cv2

cap = cv2.VideoCapture(0)
detector = HandDetector(detectionCon=0.8, maxHands=2)

while True:
    success, img = cap.read()
    hands, img = detector.findHands(img)

    if hands:
        hand1 = hands[0]
        lmList1 = hand1["lmList"]   # 21 landmarks [x, y, z]
        bbox1   = hand1["bbox"]     # x, y, w, h
        center1 = hand1["center"]   # cx, cy
        type1   = hand1["type"]     # "Left" or "Right"

        fingers1 = detector.fingersUp(hand1)  # [thumb, index, middle, ring, pinky]

        # 2D distance between two landmarks
        length, info, img = detector.findDistance(lmList1[8][0:2], lmList1[4][0:2], img)

        # 3D distance (uses z-depth)
        length3d = detector.findDistance3D(lmList1[8], lmList1[4])

        # Angle between 3 landmarks (index tip - wrist - thumb tip)
        angle = detector.findAngle(lmList1[8], lmList1[0], lmList1[4])

        if len(hands) == 2:
            hand2 = hands[1]
            lmList2 = hand2["lmList"]
            length, info, img = detector.findDistance(lmList1[8][0:2], lmList2[8][0:2], img)

    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Pose Estimation

from cvfiq.PoseModule import PoseDetector
import cv2

cap = cv2.VideoCapture(0)
detector = PoseDetector(smoothAlpha=0.5)  # 0=max smooth, 1=no smooth

while True:
    success, img = cap.read()
    img = detector.findPose(img)
    lmList, bboxInfo = detector.findPosition(img, bboxWithHands=False)

    if lmList:
        # Calculate angle at elbow (landmarks 11, 13, 15)
        angle = detector.findAngle(img, 11, 13, 15)
        # Check if angle is within range
        isCorrect = detector.angleCheck(angle, targetAngle=90, addOn=20)

    if bboxInfo:
        center = bboxInfo["center"]
        cv2.circle(img, center, 5, (255, 0, 255), cv2.FILLED)

    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Face Mesh

from cvfiq.FaceMeshModule import FaceMeshDetector
import cv2

cap = cv2.VideoCapture(0)
detector = FaceMeshDetector(maxFaces=2, refineLandmarks=False)

while True:
    success, img = cap.read()
    img, faces = detector.findFaceMesh(img)

    if faces:
        face = faces[0]

        # Get specific region landmarks
        leftEye  = detector.getRegion(face, FaceMeshDetector.LEFT_EYE)
        lips     = detector.getRegion(face, FaceMeshDetector.LIPS)

        # Detect blink
        blink = detector.blinkDetector(face)
        # {"left": bool, "right": bool, "leftEAR": float, "rightEAR": float}

        # Detect mouth open
        mouth = detector.mouthOpen(face)
        # {"open": bool, "ratio": float}

    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Selfie Segmentation (Background Removal)

from cvfiq.SelfiSegmentationModule import SelfiSegmentation
import cv2

cap = cv2.VideoCapture(0)
segmentor = SelfiSegmentation(model=1)  # 0=general, 1=landscape (faster)
imgBg = cv2.imread("background.jpg")

while True:
    success, img = cap.read()
    imgOut = segmentor.removeBG(img, imgBg=imgBg, threshold=0.1)
    # imgOut = segmentor.removeBG(img, imgBg=(0, 255, 0))  # solid color BG

    cv2.imshow("Output", imgOut)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Gesture Recognition (requires model file)

from cvfiq.GestureModule import GestureDetector
import cv2

# Download model: https://storage.googleapis.com/mediapipe-models/gesture_recognizer/gesture_recognizer/float16/latest/gesture_recognizer.task
cap = cv2.VideoCapture(0)
detector = GestureDetector(modelPath='gesture_recognizer.task')

while True:
    success, img = cap.read()
    gestures, img = detector.findGestures(img)

    for g in gestures:
        print(g["hand"], g["gesture"], g["score"])
        # Gestures: Thumb_Up, Thumb_Down, Victory, Open_Palm,
        #           Closed_Fist, Pointing_Up, ILoveYou, None

    cv2.imshow("Gesture", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Face Landmarker + Expressions (requires model file)

from cvfiq.FaceLandmarkerModule import FaceLandmarker
import cv2

# Download model: https://storage.googleapis.com/mediapipe-models/face_landmarker/face_landmarker/float16/latest/face_landmarker.task
cap = cv2.VideoCapture(0)
detector = FaceLandmarker(modelPath='face_landmarker.task')

while True:
    success, img = cap.read()
    faces, img = detector.findFaces(img)

    if faces:
        face = faces[0]

        smiling    = detector.isSmiling(face)
        blinking   = detector.isBlinking(face, eye='both')
        mouthOpen  = detector.isMouthOpen(face)
        expression = detector.getExpression(face)
        # expressions: smiling, mouth_open, blink_left, blink_right, brow_down, neutral

        # Raw blendshape value (0.0 - 1.0)
        jawScore = detector.getBlendshape(face, FaceLandmarker.JAW_OPEN)

    cv2.imshow("FaceLandmarker", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Object Detection (requires model file)

from cvfiq.ObjectDetectorModule import ObjectDetector
import cv2

# Download model: https://storage.googleapis.com/mediapipe-models/object_detector/efficientdet_lite0/int8/latest/efficientdet_lite0.tflite
cap = cv2.VideoCapture(0)
detector = ObjectDetector(modelPath='efficientdet_lite0.tflite', scoreThreshold=0.5)

while True:
    success, img = cap.read()
    objects, img = detector.findObjects(img)

    # Filter by label
    people = detector.getObjectsByLabel(objects, 'person')
    count  = detector.countObjects(objects, 'person')
    # objInfo keys: "label", "score", "bbox": (x,y,w,h), "center": (cx,cy)

    cv2.imshow("ObjectDetector", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

ArUco Marker Detection

from cvfiq.ArucoModule import ArucoDetector
import cv2

cap = cv2.VideoCapture(0)
detector = ArucoDetector(dictType='4x4_50')

while True:
    success, img = cap.read()
    markers, img = detector.findMarkers(img)
    # markerInfo keys: "id", "corners": [[x,y]x4], "center": (cx,cy)

    if markers:
        # Estimate 3D pose (distance in meters)
        poses, img = detector.estimatePose(img, markers, markerSize=0.05)
        for pose in poses:
            print(f"ID:{pose['id']}  dist:{pose['distance']:.3f}m")

    cv2.imshow("ArUco", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

# Generate a marker image and save it
detector = ArucoDetector()
markerImg = detector.generateMarker(markerId=0, size=200)
cv2.imwrite("marker_0.png", markerImg)

---

DNN Classification (no TensorFlow needed)

from cvfiq.DNNModule import DNNClassifier
import cv2

cap = cv2.VideoCapture(0)
classifier = DNNClassifier('model.onnx', labelsPath='labels.txt', imgSize=(224, 224))
# classifier.useGPU()  # enable CUDA if available

while True:
    success, img = cap.read()

    # Single prediction
    scores, index, confidence = classifier.getPrediction(img)

    # Top-3 predictions
    topK = classifier.getTopK(img, k=3)
    # [("cat", 0.92), ("dog", 0.05), ("bird", 0.01)]

    cv2.imshow("DNN", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Video Stabilizer

from cvfiq.VideoStabilizerModule import VideoStabilizer
import cv2

cap = cv2.VideoCapture(0)
stabilizer = VideoStabilizer(smoothRadius=15, border='black')
# border options: 'black', 'replicate', 'reflect'

while True:
    success, img = cap.read()
    imgStab = stabilizer.stabilize(img)

    smoothness = stabilizer.getSmoothness()  # 0.0 (shaky) to 1.0 (smooth)

    cv2.imshow("Stabilized", imgStab)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break

stabilizer.reset()  # reset when switching video source
cap.release()
cv2.destroyAllWindows()

---

Color Detection

from cvfiq.ColorModule import ColorFinder
import cv2

cap = cv2.VideoCapture(0)
myColorFinder = ColorFinder(trackBar=False)

# Custom color HSV range
hsvVals = {'hmin': 10, 'smin': 55, 'vmin': 215, 'hmax': 42, 'smax': 255, 'vmax': 255}

while True:
    success, img = cap.read()

    # Single color
    imgColor, mask = myColorFinder.update(img, 'red')   # built-in: red, green, blue
    imgColor, mask = myColorFinder.update(img, hsvVals)  # custom HSV

    # Multiple colors at once
    results = myColorFinder.updateMulti(img, ['red', 'green', 'blue'])
    # results["red"]["imgColor"], results["red"]["mask"]

    # Save / load custom color profiles
    myColorFinder.saveColor('orange', hsvVals, 'colors.json')
    loaded = myColorFinder.loadColor('orange', 'colors.json')

    cv2.imshow("Color", imgColor)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

FPS Counter

import cvfiq
import cv2

fpsReader = cvfiq.FPS(avgCount=10)  # average over 10 frames
cap = cv2.VideoCapture(0)

while True:
    success, img = cap.read()
    fps, img = fpsReader.update(img, pos=(20, 50), color=(0, 255, 0), scale=3, thickness=3)
    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

PID Controller

from cvfiq.PIDModule import PID
from cvfiq.FaceDetectionModule import FaceDetector
import cv2

cap = cv2.VideoCapture(0)
detector = FaceDetector()

# [Kp, Ki, Kd], target, iLimit = integral windup clamp
xPID = PID([1, 0.0001, 0.5], targetVal=320, iLimit=[-500, 500])
yPID = PID([1, 0.0001, 0.5], targetVal=240, axis=1, limit=[-100, 100], iLimit=[-500, 500])

while True:
    success, img = cap.read()
    img, bboxs = detector.findFaces(img)

    if bboxs:
        cx, cy = bboxs[0]["center"]
        xVal = int(xPID.update(cx))
        yVal = int(yPID.update(cy))
        xPID.draw(img, [cx, cy])
        yPID.draw(img, [cx, cy])
    else:
        xPID.reset()  # reset state when face lost
        yPID.reset()

    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Live Plot

import cvfiq
import cv2
import math

xPlot = cvfiq.LivePlot(w=640, h=480, yLimit=[-100, 100])
x = 0

while True:
    x = (x + 1) % 360
    val = int(math.sin(math.radians(x)) * 100)
    imgPlot = xPlot.update(val, color=(255, 0, 255))
    cv2.imshow("Plot", imgPlot)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cv2.destroyAllWindows()

---

Stack Images

import cvfiq
import cv2

cap = cv2.VideoCapture(0)

while True:
    success, img = cap.read()
    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    imgList = [img, img, imgGray, img, imgGray, img]
    stackedImg = cvfiq.stackImages(imgList, cols=3, scale=0.4)
    cv2.imshow("Stacked", stackedImg)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Arduino Serial Communication

from cvfiq.SerialModule import SerialObject
import time

# Auto-detect Arduino, or specify port
arduino = SerialObject(portNo=None, baudRate=9600, digits=3, timeout=1)

while True:
    # Send data: "$001001000" (3 digits per value)
    arduino.sendData([1, 1, 0])

    # Receive data (with timeout — won't hang)
    data = arduino.getData()
    print(data)

    time.sleep(0.1)

---

Classification (Teachable Machine)

from cvfiq.ClassificationModule import Classifier
import cv2

cap = cv2.VideoCapture(0)
classifier = Classifier('Model/keras_model.h5', 'Model/labels.txt', imgSize=224)

while True:
    success, img = cap.read()

    # Returns: (all scores list, top index, confidence)
    scores, index, confidence = classifier.getPrediction(img)
    print(f"Predicted: index={index}  confidence={confidence:.2f}")

    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

---

Utility Functions

import cvfiq
import cv2

img = cv2.imread("image.jpg")

# Stack multiple images into one window
stacked = cvfiq.stackImages([img, img, img], cols=3, scale=0.5)

# Fancy bounding box with corner lines
img = cvfiq.cornerRect(img, bbox=(100, 100, 200, 150))

# Find contours with area filter
imgCon, contours = cvfiq.findContours(img, imgPre, minArea=1000, filter=4)  # filter=4: rectangle only

# Overlay transparent PNG
img = cvfiq.overlayPNG(imgBack=img, imgFront=imgPNG, pos=[50, 50])

# Rotate image
img = cvfiq.rotateImage(img, angle=45, scale=1)

# Text with background rectangle
img, rect = cvfiq.putTextRect(img, "Hello", pos=(50, 50), scale=2, thickness=2)

---

Module Overview

Module	Class	Description
FaceDetectionModule	FaceDetector	Face detection + 6 keypoints
FaceMeshModule	FaceMeshDetector	468 face landmarks, blink & mouth detection
HandTrackingModule	HandDetector	21 hand landmarks, finger detection, 2D/3D distance, angle
PoseModule	PoseDetector	Body pose estimation, angle, smoothing
SelfiSegmentationModule	SelfiSegmentation	Real-time background removal
GestureModule	GestureDetector	Built-in gesture recognition (Tasks API)
FaceLandmarkerModule	FaceLandmarker	478 landmarks + 52 expression blendshapes (Tasks API)
ObjectDetectorModule	ObjectDetector	80-class object detection (Tasks API)
ArucoModule	ArucoDetector	ArUco marker detection + 3D pose estimation
DNNModule	DNNClassifier	ONNX/TFLite classification via OpenCV DNN
VideoStabilizerModule	VideoStabilizer	Real-time video stabilization
ColorModule	ColorFinder	HSV color detection, save/load profiles, multi-color
ClassificationModule	Classifier	Teachable Machine model inference
FPS	FPS	Averaged FPS counter
PIDModule	PID	PID controller with windup clamp
PlotModule	LivePlot	Real-time graph in OpenCV window
SerialModule	SerialObject	Arduino serial communication
Utils	—	stackImages, cornerRect, findContours, overlayPNG, rotateImage, putTextRect

---

License

MIT License — see LICENSE.txt