Face Detection and Recognition

Mon Oct 14 2024 16:03:20 GMT+0000 (Coordinated Universal Time)

import tkinter as tk from tkinter import simpledialog, messagebox import sys import os import cv2 import numpy as np sys.path.append('/mnt/data') haar_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml") def face_detect(): try: folder_name = simpledialog.askstring("Input", "Enter the folder name:") if folder_name: dataset = "dataset1" name = folder_name path = os.path.join(dataset, name) print(os.path.isdir(path)) if not os.path.exists(path): os.makedirs(path) (width, height) = (130, 100) cam = cv2.VideoCapture(0) count = 1 while count < 301: _, img = cam.read() gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) face = haar_cascade.detectMultiScale(gray_img, 1.3, 4) if len(face) > 0: for (x, y, w, h) in face: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 255, 0), 2) print(count) print("Person Detected") cv2.putText(img, 'Person Detected', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 255, 0), 2, cv2.LINE_AA) else: cv2.putText(img, 'No Person Detected', (50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1, (0, 0, 255), 2, cv2.LINE_AA) print("NO Person Detected") cv2.imshow("FaceDetection", img) key = cv2.waitKey(10) if key == 27: break if len(face) > 0: for (x, y, w, h) in face: face_only = img[y:y + h, x:x + w] resize_img = cv2.resize(face_only, (width, height)) cv2.imwrite("%s/%s.jpg" % (path, count), resize_img) count += 1 print("Completed Face Detection") cam.release() cv2.destroyAllWindows() else: messagebox.showwarning("Input Error", "Folder name cannot be empty!") except Exception as e: print(f"Error occurred[Detect]: {str(e)}") def face_recognize(): try: datasets = 'dataset1' print('Training...') (images, labels, names, id) = ([], [], {}, 0) for (subdirs, dirs, files) in os.walk(datasets): for subdir in dirs: names[id] = subdir subjectpath = os.path.join(datasets, subdir) print("Subject Path:", subjectpath) for filename in os.listdir(subjectpath): path = os.path.join(subjectpath, filename) print("Image Path:", path) label = id image = cv2.imread(path, cv2.IMREAD_GRAYSCALE) if image is None: print("Error loading image:", path) continue print("Image shape:", image.shape) images.append(image) labels.append(int(label)) id += 1 (images, labels) = [np.array(lis) for lis in [images, labels]] print("Number of images loaded:", len(images)) print("Labels:", labels) (width, height) = (130, 100) model = cv2.face.LBPHFaceRecognizer_create() model.train(images, labels) webcam = cv2.VideoCapture(0) cnt = 0 while True: ret, img = webcam.read() if not ret: print("Failed to capture image from webcam") break gray_img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) faces = haar_cascade.detectMultiScale(gray_img, 1.3, 5) for (x, y, w, h) in faces: cv2.rectangle(img, (x, y), (x + w, y + h), (0, 0, 255), 2) face_crop = gray_img[y:y + h, x:x + w] resized_face = cv2.resize(face_crop, (width, height)) prediction = model.predict(resized_face) if prediction[1] < 800: cv2.putText(img, "%s - %.0f" % (names[prediction[0]], prediction[1]), (x - 10, y - 10), cv2.FONT_HERSHEY_PLAIN, 2, (0, 255, 0)) print(names[prediction[0]]) cnt = 0 else: cnt += 1 cv2.putText(img, 'Unknown', (x - 10, y - 10), cv2.FONT_HERSHEY_PLAIN, 2, (0, 0, 255)) if cnt > 100: print("Unknown Person") cv2.imwrite("unknown.jpg", img) cnt = 0 cv2.imshow("Face Recognition", img) key = cv2.waitKey(10) if key == 27: break webcam.release() cv2.destroyAllWindows() except Exception as e: print(f"Error occurred[Recognize]: {str(e)}") root = tk.Tk() root.title("Face Detection & Recognition") root.geometry("400x200") label = tk.Label(root, text="Choose an Option", font=("Arial", 16)) label.pack(pady=20) detect_button = tk.Button(root, text="Face Detect", command=face_detect, width=20, height=2) detect_button.pack(pady=10) recognize_button = tk.Button(root, text="Face Recognition", command=face_recognize, width=20, height=2) recognize_button.pack(pady=10) root.mainloop()

https://www.thiscodeworks.com/Face_DandR

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Face Detection and Recognition

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