face = face_detector.detectMultiScale(gray, 1.1, 5, cv2.CASCADE_SCALE_IMAGE, (100, 100), (300, 300))

As an AI language model, I do not have access to your complete code. However, I can explain the purpose and parameters of the code snippet you provided.

This line of code is using the OpenCV library to detect faces in an image. The detectMultiScale() function takes several parameters:

- gray: This is the grayscale version of the input image.
- 1.1: This is the scale factor. It determines how much the image size is reduced at each image scale. A smaller value will increase the detection time but also increase accuracy.
- 5: This is the minimum number of neighboring rectangles that must also pass the threshold to be considered a face.
- cv2.CASCADE_SCALE_IMAGE: This is the detection flags, which determine how the function processes the input image. CASCADE_SCALE_IMAGE means that the image is scaled progressively to improve detection.
- (100, 100): This is the minimum size of the detected face.
- (300, 300): This is the maximum size of the detected face.

Together, these parameters help the function to detect faces in the image and return their locations as rectangles.