2. Files
01_create_dataset.py
02_create_clusters.py
03_rearrange_data.py
04_train_model.py
05_make_predictions.py

3. Details

01_create_dataset.py
This code captures grayscale images of a person's face using a webcam, saves them, and stores corresponding metadata (customer name, image path) in a SQLite database.

02_create_clusters.py
This code seems to be automating the process of organizing images into clusters based on their features extracted from a VGG16 model. It allows you to identify potentially fake faces for manual review and removal, aiding in maintaining a clean dataset.

03_rearrange_data.py
This code clears out the 'dataset' directory, then populates it with images from 'dataset-clusters'. After that, it removes 'dataset-clusters'. Finally, it checks the SQLite database for records whose associated images are not in the 'dataset' folder, deletes those records, and prints a message for each deletion. This process ensures data integrity and cleanliness in the dataset.

04_train_model.py

This code trains a LBPH (Local Binary Patterns Histograms) face recognizer using images from a dataset. It detects faces in images using a Haar Cascade Classifier, extracts face samples, and their corresponding labels. The LBPH recognizer is then trained with these samples. If no faces are found in the dataset, it prints a corresponding message. Finally, the trained model is saved for future use.

05_make_predictions.py
This code utilizes a pre-trained LBPH (Local Binary Patterns Histograms) face recognizer model along with a Haar Cascade classifier for face detection. It captures frames from a webcam feed, converts them to grayscale, and detects faces. For each detected face, it predicts its identity and confidence level using the LBPH model. If the confidence level is above a threshold, it draws a rectangle around the face and displays the predicted ID along with the confidence percentage as a name tag. The process continues until the user presses 'q' to exit the loop, releasing the camera and closing all OpenCV windows.

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4. Assignment (corrected)

Instead of displaying the ID, fetch and display info (Full Name in this case) of the person whose face is recognized based on the predicted ID (UID) from the database.

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5. Querying SQLite DB

Assuming SQLite3 is installed, start by typing the command 'sqlite3 customer_faces_data.db'. You'll then get a prompt sqlite>

Go ahead and type commands like pragma table_info (customers); ...