Real Time Age and Gender Detection with OpenCV

This code describes the process of face detection on an image using OpenCV and some deep learning techniques, along with drawing a bounding box around the detected face. It also uses pre-trained models to predict gender and age. Here’s a step-by-step explanation:

1. Obtaining Confidence Values: The confidence values of detected faces are obtained from the detection matrix. This value represents the accuracy of the face detection. The code retrieves this value using detections[0, 0, i, 2], where i indicates the face detection index and 2 refers to the column containing the confidence value.

2. Filtering Based on Confidence Values: If the confidence value is above a certain threshold (e.g., 0.7), this detection is accepted, and bounding boxes are calculated.

3. Calculating Bounding Boxes: The expression detections[0, 0, i, 3:7] provides the coordinates of the bounding box (x1, y1, x2, y2). These coordinates are normalized, meaning they have values between 0 and 1. Therefore, the actual image coordinates are obtained by multiplying with the frame's width and height.

4. Drawing Bounding Boxes: The cv2.rectangle() function is used to draw a rectangle around the detected face. The color and thickness are set to visualize the bounding box.

5. Age and Gender Prediction: Two pre-trained models are used for age and gender prediction: age_net and gender_net. These models are loaded using cv2.dnn.readNet() and input data in the appropriate format is created using the blobFromImage() function. After creating the blob, it is assigned to the model's input layer using setInput(), and predictions are obtained using the forward() method. The np.argmax() function is then used to get the class label with the highest probability, which is used for gender and age prediction.

6. Visualizing Results: The predicted age and gender label are written onto the image using the cv2.putText() function. Features like font, size, and color are set to add the text to the image.

7. Defining Models and Parameters: Average values and other model parameters for the age and gender models are specified. The age_list and gender_list variables are used to interpret the model output.

Overall, the code involves face detection, drawing bounding boxes, and predicting the age and gender of these faces. The predicted information is displayed on the detected faces in the image.