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Commit2f2d42c

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add facial recognition system tutorial
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‎README.md

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@@ -98,6 +98,7 @@ This is a repository of all the tutorials of [The Python Code](https://www.thepy
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-[How to Upscale Images using Stable Diffusion in Python](https://www.thepythoncode.com/article/upscale-images-using-stable-diffusion-x4-upscaler-huggingface). ([code](machine-learning/stable-diffusion-upscaler))
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-[Real-Time Vehicle Detection, Tracking and Counting in Python](https://thepythoncode.com/article/real-time-vehicle-tracking-and-counting-with-yolov8-opencv). ([code](https://github.com/python-dontrepeatyourself/Real-Time-Vehicle-Detection-Tracking-and-Counting-in-Python/))
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-[How to Cartoonify Images in Python](https://thepythoncode.com/article/make-a-cartoonifier-with-opencv-in-python). ([code](machine-learning/cartoonify-images))
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-[How to Make a Facial Recognition System in Python](https://thepythoncode.com/article/create-a-facial-recognition-system-in-python). ([code](machine-learning/facial-recognition-system))
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-[Building a Speech Emotion Recognizer using Scikit-learn](https://www.thepythoncode.com/article/building-a-speech-emotion-recognizer-using-sklearn). ([code](machine-learning/speech-emotion-recognition))
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-[How to Convert Speech to Text in Python](https://www.thepythoncode.com/article/using-speech-recognition-to-convert-speech-to-text-python). ([code](machine-learning/speech-recognition))
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-[Top 8 Python Libraries For Data Scientists and Machine Learning Engineers](https://www.thepythoncode.com/article/top-python-libraries-for-data-scientists).
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#[How to Make a Facial Recognition System in Python](https://thepythoncode.com/article/create-a-facial-recognition-system-in-python)
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importcv2,numpyasnp,face_recognition,os,tkinterastk
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fromtkinterimportfiledialog
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# Initialize empty lists to store images and people's names.
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known_faces= []
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face_labels= []
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# Get a list of all images in the TrainingImages directory.
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image_files=os.listdir("TrainingImages")
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# Loop through the images in the directory.
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forimage_nameinimage_files:
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# Read each image and add it to the known_faces list.
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current_image=cv2.imread(f'TrainingImages/{image_name}')
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known_faces.append(current_image)
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# Extract the person's name by removing the file extension and add it to the face_labels list.
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face_labels.append(os.path.splitext(image_name)[0])
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# Function to get face encodings from a list of images.
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defget_face_encodings(images):
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encoding_list= []
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forimageinimages:
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# Convert the image to RGB format. RGB is Red Green Blue.
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image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
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# Get the face encoding for the first face found in the image.
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face_encoding=face_recognition.face_encodings(image)[0]
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encoding_list.append(face_encoding)
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returnencoding_list
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# Get face encodings for known images.
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known_face_encodings=get_face_encodings(known_faces)
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# Function to handle image selection and recognition
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defselect_and_recognize_image():
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# Use a file dialog to let the user select an image.
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selected_file=filedialog.askopenfilename()
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ifselected_file:
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# Read the selected image.
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selected_image=cv2.imread(selected_file)
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# Convert the image to RGB format.
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selected_image_rgb=cv2.cvtColor(selected_image,cv2.COLOR_BGR2RGB)
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# Get face encodings for the selected image.
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selected_face_encodings=face_recognition.face_encodings(selected_image_rgb)
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match_found=False# Flag to track if a match is found.
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ifnotselected_face_encodings:
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print("No faces found in the selected image.")
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else:
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# Loop through the detected faces in the selected image.
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forface_encodinginselected_face_encodings:
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# Compare the current face encoding with the known encodings.
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matches=face_recognition.compare_faces(known_face_encodings,face_encoding)
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face_distances=face_recognition.face_distance(known_face_encodings,face_encoding)
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# Find the index of the best match. That is, the best resemblance.
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best_match_index=np.argmin(face_distances)
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ifmatches[best_match_index]:
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# If a match is found, get the name of the recognized person.
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recognized_name=face_labels[best_match_index].upper()
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# Draw a green rectangle around the recognized face.
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top,right,bottom,left=face_recognition.face_locations(selected_image_rgb)[0]
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cv2.rectangle(selected_image, (left,top), (right,bottom), (0,255,0),2,)
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# Display the name below the face.
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cv2.putText(selected_image,recognized_name, (left+6,bottom-6),cv2.FONT_HERSHEY_COMPLEX,0.5,
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(0,255,0),2)
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match_found=True# Match found flag.
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break# Exit loop as soon as a match is found.
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ifnotmatch_found:
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# If no match is found, draw a red rectangle and display No Match.
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top,right,bottom,left=face_recognition.face_locations(selected_image_rgb)[0]
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cv2.rectangle(selected_image, (left,top), (right,bottom), (0,0,255),2)
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cv2.putText(selected_image,"No match", (left+6,bottom-6),cv2.FONT_HERSHEY_COMPLEX,1,
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(0,0,255),2)
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# Show the image with the rectangle and name.
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cv2.imshow("Recognized Image",selected_image)
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known_faces.clear()# To prevent the program from slowing down due to excess unnecessary encodings.
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cv2.waitKey(0)
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cv2.destroyAllWindows()
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# Create the main application window.
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root=tk.Tk()
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root.title("Face Recognition Program")
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# Create a button to select an image for recognition.
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select_button=tk.Button(root,text="Select Image for Recognition",command=select_and_recognize_image)
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select_button.pack(pady=10)
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# Function to quit the application.
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defquit_app():
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root.quit()
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# Create a quit button to exit the application.
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quit_button=tk.Button(root,text="Quit",command=quit_app)
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quit_button.pack(pady=10)
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# Start the Tkinter event loop.
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root.mainloop()
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importtkinterastk,numpyasnp,cv2,os,face_recognition
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fromdatetimeimportdatetime
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# Initialize empty lists to store images and people's names.
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known_faces= []
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face_labels= []
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# Get a list of all images in the TrainingImages directory.
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image_files=os.listdir("TrainingImages")
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# Loop through the images in the directory.
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forimage_nameinimage_files:
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# Read each image and add it to the known_faces list.
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current_image=cv2.imread(f'TrainingImages/{image_name}')
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known_faces.append(current_image)
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# Extract the person's name by removing the file extension and add it to the face_labels list.
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face_labels.append(os.path.splitext(image_name)[0])
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# Function to get face encodings from a list of images.
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defget_face_encodings(images):
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encoding_list= []
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forimageinimages:
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# Convert the image to RGB format. RGB is Red Green Blue.
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image=cv2.cvtColor(image,cv2.COLOR_BGR2RGB)
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# Get the face encoding for the first face found in the image.
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face_encoding=face_recognition.face_encodings(image)[0]
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encoding_list.append(face_encoding)
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returnencoding_list
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# Define a function to document the recognized face.
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defdocument_recognised_face(name,filename='records.csv'):
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# Get the current date in the YYYY-MM-DD format.
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capture_date=datetime.now().strftime("%Y-%m-%d")
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# Check if the specified CSV file exists.
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ifnotos.path.isfile(filename):
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# If the file doesn't exist, create it and write the header.
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withopen(filename,'w')asf:
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f.write('Name,Date,Time')# Create the file and write the header.
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# Open the CSV file for reading and writing ('r+')
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withopen(filename,'r+')asfile:
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# Read all lines from the file into a list.
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lines=file.readlines()
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# Extract the names from existing lines in the CSV.
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existing_names= [line.split(",")[0]forlineinlines]
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# Check if the provided name is not already in the existing names.
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ifnamenotinexisting_names:
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# Get the current time in the HH:MM:SS format.
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now=datetime.now()
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current_time=now.strftime("%H:%M:%S")
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# Write the new entry to the CSV file including name, capture date, and time.
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file.write(f'\n{name},{capture_date},{current_time}')
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# Get face encodings for known images.
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known_face_encodings=get_face_encodings(known_faces)
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# Function to start the Facial recognition program.
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defstart_recognition_program():
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# Open a webcam for capturing video. If you are using your computer's webcam, change 1 to 0.
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# If using an external webcam, leave it as 1.
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video_capture=cv2.VideoCapture(1)
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whileTrue:
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# Read a frame from the webcam.
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frame=video_capture.read()
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# Check if the frame is not None (indicating a successful frame capture).
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ifframeisnotNone:
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frame=frame[1]# The frame is usually the second element of the tuple returned by video_capture.read().
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# Resize the image to a smaller size.
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resized_frame=cv2.resize(frame, (0,0),None,0.25,0.25)
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resized_frame=cv2.cvtColor(resized_frame,cv2.COLOR_BGR2RGB)
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# Detect faces in the current frame.
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face_locations=face_recognition.face_locations(resized_frame)
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# Get face encodings for the faces detected in the current frame.
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current_face_encodings=face_recognition.face_encodings(resized_frame,face_locations)
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# Loop through the detected faces in the current frame.
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forface_encoding,locationinzip(current_face_encodings,face_locations):
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# Compare the current face encoding with the known encodings.
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matches=face_recognition.compare_faces(known_face_encodings,face_encoding)
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face_distances=face_recognition.face_distance(known_face_encodings,face_encoding)
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# Find the index of the best match. That is, the best resemblance.
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best_match_index=np.argmin(face_distances)
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ifmatches[best_match_index]:
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# If a match is found, get the name of the recognized person.
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recognized_name=face_labels[best_match_index].upper()
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# Extract face location coordinates.
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top,right,bottom,left=location
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top,right,bottom,left=top*4,right*4,bottom*4,left*4
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# Draw a rectangle around the recognized face.
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cv2.rectangle(frame, (left,top), (right,bottom), (0,255,0),2)
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# Draw a filled rectangle and display the name above the face.
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cv2.rectangle(frame, (left,bottom-35), (right,bottom), (0,255,0),cv2.FILLED)
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cv2.putText(frame,recognized_name, (left+6,bottom-6),cv2.FONT_HERSHEY_COMPLEX,1,
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(255,255,255),2)
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document_recognised_face(recognized_name)
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# Display the image with recognized faces.
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cv2.imshow("Webcam",frame)
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# Check for key press
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key=cv2.waitKey(1)&0xFF
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# Check if the 'q' key is pressed to exit the program.
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ifkey==ord('q'):
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break
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# Release the video capture and close all OpenCV windows.
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video_capture.release()
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cv2.destroyAllWindows()
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# Create the main application window.
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root=tk.Tk()
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root.title("Face Recognition Program")
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# Create a label
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label=tk.Label(root,text="Click the button to start the facial recognition program")
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label.pack(pady=10)
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# Create a button to start the program
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start_button=tk.Button(root,text="Start Recognition",command=start_recognition_program)
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start_button.pack(pady=10)
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# Function to quit the application. This is for quitting the entire program. To quit the webcam stream, hit q.
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defquit_app():
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root.quit()
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cv2.destroyAllWindows()
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# Create a quit button to exit the application.
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exit_button=tk.Button(root,text="Close",command=quit_app)
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exit_button.pack(pady=10)
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# Start the Tkinter event loop.
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root.mainloop()
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cmake
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dlib==19.18.0
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face-recognition
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opencv-python

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