[Rejected] Face And Eye Detection System
This Is Python Code That Can Detect Face And Eye In Streaming Camera Using Deep Learning.
Screenshots
Item Description
The above code is a Python script that uses the OpenCV library and Tkinter GUI toolkit to capture the video from the webcam, detect faces and eyes in real-time, and display the video stream in a GUI window.
The code first loads the pre-trained face and eye detection classifiers from the OpenCV library. It then initializes the webcam using the cv2.VideoCapture() function.
Next, the code creates a Tkinter window using the tk.Tk() function and sets the title of the window to "Webcam". It creates a canvas using the tk.Canvas() function with a width of 640 pixels and a height of 480 pixels, and packs the canvas to the window using the pack() method.
The code defines a function update_frame() that reads a frame from the webcam using the cap.read() function. If the frame is successfully read, the function converts the frame to grayscale using cv2.cvtColor() function, detects faces in the grayscale frame using the face_cascade.detectMultiScale() function, and draws rectangles around the detected faces and eyes using the cv2.rectangle() function. It then converts the frame to an ImageTk object and displays it on the canvas using the canvas.create_image() function.
The update_frame() function schedules the next frame update using the root.after() method with a delay of 10 milliseconds. The first frame update is scheduled using the same method with a delay of 0 milliseconds.
Finally, the code starts the Tkinter event loop using the root.mainloop() method. When the Tkinter window is closed, the code releases the webcam using the cap.release() function.
Features
The above code has several features that make it a useful tool for real-time face and eye detection in streaming video from a webcam:
OpenCV library: The code uses the OpenCV library, which is a popular computer vision library that provides various functions and tools for image and video processing. In this code, we use the pre-trained face and eye detection classifiers from the OpenCV library to detect faces and eyes in real-time video stream.
Tkinter GUI toolkit: The code uses the Tkinter GUI toolkit, which is a standard Python library for creating graphical user interfaces. It allows us to create a window and display the video stream in a canvas, which makes it easy to visualize the results of the face and eye detection algorithm.
Real-time video stream: The code captures the video stream from the webcam in real-time and updates the canvas with the latest frame. This allows us to see the results of the face and eye detection algorithm in real-time.
Face and eye detection: The code uses the pre-trained Haar cascades for face and eye detection from the OpenCV library. It applies these cascades to each frame of the video stream and detects faces and eyes in the stream. It then draws rectangles around the detected faces and eyes to highlight them in the video stream.
Efficiency: The code is efficient in terms of processing power and memory usage. It uses the
after()method of Tkinter to schedule the next frame update, which helps to minimize the CPU usage. It also converts the video stream to grayscale before applying the Haar cascades, which reduces the processing time and memory usage compared to applying the cascades to the color video stream.
Overall, the above code provides a simple and efficient tool for real-time face and eye detection in streaming video from a webcam. It can be easily customized and extended for other applications in computer vision and image processing.
Information
| Category | Scripts & Code / Python |
| First Release | 7 April 2023 |
| Last updated | 7 April 2023 |
| Files included | .py |
| Frameworks | Python |
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