How to Install Pyresearch: A Step-by-Step Guide

You can simply use pip to install the latest version of pyresearch.

          

from pyresearch.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 - "id","bbox","score","center"
        center = bboxs[0]["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()

from pyresearch.FaceMeshModule import FaceMeshDetector
import cv2

cap = cv2.VideoCapture(0)
detector = FaceMeshDetector(maxFaces=2)
while True:
    success, img = cap.read()
    img, faces = detector.findFaceMesh(img)
    if faces:
        print(faces[0])
    cv2.imshow("Image", img)
    if cv2.waitKey(1) & 0xFF == ord('q'):
        break
cap.release()
cv2.destroyAllWindows()

import pyresearch
import cv2

fpsReader = pyresearch.FPS()

cap = cv2.VideoCapture(0)


cap.set(3, 1280)

cap.set(4, 720)

while True:

    success, img = cap.read()
   
    fps, img = fpsReader.update(img,pos=(50,80),color=(0,255,0),scale=5,thickness=5)
   
    cv2.imshow("Image", img)
   
    if cv2.waitKey(1) & 0xFF == ord('q'):
   
        break

cap.release()
cv2.destroyAllWindows()

import pyresearch
import cv2

cap = cv2.VideoCapture(0)
cap.set(3, 1280)
cap.set(4, 720)

while True:
    success, img = cap.read()
    imgGray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
    imgList = [img, img, imgGray, img, imgGray, img,imgGray, img, img]
    stackedImg = pyresearch.stackImages(imgList, 3, 0.4)

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

from pyresearch.HandTrackingModule import HandDetector
import cv2

cap = cv2.VideoCapture(0)

detector = HandDetector(detectionCon=0.8, maxHands=2)

while True:
    # Get image frame

    success, img = cap.read()

    # Find the hand and its landmarks

    hands, img = detector.findHands(img)  # with draw

    # hands = detector.findHands(img, draw=False)  # without draw

    if hands:

        # Hand 1

        hand1 = hands[0]

        lmList1 = hand1["lmList"]  # List of 21 Landmark points

        bbox1 = hand1["bbox"]  # Bounding box info x,y,w,h

        centerPoint1 = hand1['center']  # center of the hand cx,cy

        handType1 = hand1["type"]  # Handtype Left or Right

        fingers1 = detector.fingersUp(hand1)

        if len(hands) == 2:
            # Hand 2
            hand2 = hands[1]
            lmList2 = hand2["lmList"]  # List of 21 Landmark points
            bbox2 = hand2["bbox"]  # Bounding box info x,y,w,h
            centerPoint2 = hand2['center']  # center of the hand cx,cy
            handType2 = hand2["type"]  # Hand Type "Left" or "Right"

            fingers2 = detector.fingersUp(hand2)

            # Find Distance between two Landmarks. Could be same hand or different hands
            length, info, img = detector.findDistance(lmList1[8], lmList2[8], img)  # with draw
            # length, info = detector.findDistance(lmList1[8], lmList2[8])  # with draw
    # Display
    cv2.imshow("Image", img)
    cv2.waitKey(1)
cap.release()
cv2.destroyAllWindows()

from pyresearch.PoseModule import PoseDetector
import cv2

cap = cv2.VideoCapture(0)
detector = PoseDetector()
while True:
    success, img = cap.read()
    img = detector.findPose(img)
    lmList, bboxInfo = detector.findPosition(img, bboxWithHands=False)
    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()

import cv2
from pyresearch.SelfiSegmentationModule import SelfiSegmentation

# connecting the internal camera (first camera index will be 0, it is the default)
cap = cv2.VideoCapture(0)

# extracting the camera capture size
width, height = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH )), int( cap.get(cv2.CAP_PROP_FRAME_HEIGHT ))

# loading and resizing the background image
background_image = cv2.resize(cv2.imread("bg_image.jpeg"), (width, height)) 

# creating segmentation instance for taking the foreground (the person).
segmentor = SelfiSegmentation()

# iterating the camera captures
while True:
    # Reading the captured images from the camera
    ret, frame = cap.read()

    # segmenting the image
    segmentated_img = segmentor.removeBG(frame, background_image, threshold=0.9)

    # concatenating the images horizontally
    concatenated_img = cv2.hconcat([frame, segmentated_img])

    #cv2.imshow("Camera Capture", concatenated_img)
    cv2.imshow("Camera Live", concatenated_img)

    # ending condition
    if cv2.waitKey(1) == ord('q'):
        break

# relasing the sources
cap.release()
cv2.destroyAllWindows()

import argparse
from pyresearch.heatmap_and_track import process_video

def main():
    # Define the arguments as a dictionary
    args = {
        "source_weights_path": "yolov8s.pt",
        "source_video_path": "people-walking.mp4",
        "target_video_path": "output.mp4",
        "confidence_threshold": 0.35,
        "iou_threshold": 0.5,
        "heatmap_alpha": 0.5,
        "radius": 25,
        "track_threshold": 0.35,
        "track_seconds": 5,
        "match_threshold": 0.99,
        "display": True,
    }

    # Convert the dictionary to an argparse Namespace object
    args_namespace = argparse.Namespace(**args)

    # Call the process_video function with the Namespace object
    process_video(args_namespace)

if __name__ == "__main__":
    main()




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