Tipps zur OPEN CV – Gesichtererkennung

import cv2
from pathlib import Path

# Load pre-trained model 'haarcascade_frontalface.xml" for face recognition / classification
# von opencv GitHub Repository
faceCascade = cv2.CascadeClassifier('../haarcascade_frontalface.xml')

def saveImage(image, userName, userId, imgId):
    # Create a folder with the name as userName
    Path("dataset/{}".format(userName)).mkdir(parents=True, exist_ok=True)
    # Save the images inside the previously created folder
    cv2.imwrite("dataset/{}/{}_{}.jpg".format(userName, userId, imgId), image)
    print("[INFO] Image {} has been saved in folder : {}".format(
        imgId, userName))

# Create object for default camera 0
vc = cv2.VideoCapture(0)

print("Enter the id and name of the person")

userId = input()
userName = input()

count = 1

while True:
    # Capture the frame/image
    _, img = vc.read()

    # Copy the original Image
    originalImg = img.copy()

    # Get the gray version of our image
    gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Get the coordinates of the location of the face in the picture
    faces = faceCascade.detectMultiScale(gray_img,
                                         scaleFactor=1.2,
                                         minNeighbors=5,
                                         minSize=(50, 50))

    # Draw a rectangle at the location of the coordinates
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        coords = [x, y, w, h]

    # Show the image
    cv2.imshow("Identified Face", img)

    # Wait for user keypress
    key = cv2.waitKey(1) & 0xFF

    # Check if the pressed key is 'k' or 'q'
    if key == ord('s'):
        # If count is less than 5 then save the image
        if count <= 5:
            roi_img = originalImg[coords[1] : coords[1] + coords[3], coords[0] : coords[0] + coords[2]]
            saveImage(roi_img, userName, userId, count)
            count += 1
        else:
            break
    # If q is pressed break out of the loop
    elif key == ord('q'):
        break

print("[INFO] Dataset has been created for {}".format(userName))

# Stop the video camera
vc.release()
# Close all Windows
cv2.destroyAllWindows()

import cv2
import json

faceCascade = cv2.CascadeClassifier("../haarcascade_frontalface.xml")

# Call the trained model yml file to recognize faces
recognizer = cv2.face.LBPHFaceRecognizer_create()

# First train with train_model.ipynb
recognizer.read("../training.yml")

# Names corresponding to each id
# Die Reihenfolge der Namen vom Verzeichnis aus zu laden funktioniert nicht. 
# Besser man speicher sich ein Array in ein JSON Dokument
# names.json: [ "Name1", "Name2", "Name3","..."]

with open("../names.json") as f:
    names = json.load(f)

video_capture = cv2.VideoCapture(0)

while True:

    _, img = video_capture.read()

    gray_image = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    faces = faceCascade.detectMultiScale(
        gray_image, scaleFactor=1.2, minNeighbors=5, minSize=(100, 100)
    )

    # Try to predict the face and get the id
    # Then check if id == 1 or id == 2
    # Accordingly add the names
    for (x, y, w, h) in faces:
        cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2)
        id, _ = recognizer.predict(gray_image[y : y + h, x : x + w])
        if id:
            cv2.putText(
                img,
                names[id-1],
                (x, y - 4),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.8,
                (0, 255, 0),
                1,
                cv2.LINE_AA,
            )
        else:
            cv2.putText(
                img,
                "Unknown",
                (x, y - 4),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.8,
                (255, 0, 0),
                1,
                cv2.LINE_AA,
            )

    cv2.imshow("Recognize", img)

    if cv2.waitKey(1) & 0xFF == ord("q"):
        break

video_capture.release()
cv2.destroyAllWindows()

pip3 install opencv-contrib-python==4.5.3.56

sudo apt install python3-h5py # (https://docs.h5py.org/en/stable/)
sudo apt install libatlas-base-dev # (https://packages.debian.org/de/sid/libatlas-base-dev)
pip install -U numpy # (Aktuelle Version von numpy)