src/detect_mask_image.py

# USAGE
# python detect_mask_image.py --image images/pic1.jpeg

import argparse
import os

import cv2
import numpy as np

# import the necessary packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array


def mask_image():
    # construct the argument parser and parse the arguments
    ap = argparse.ArgumentParser()
    ap.add_argument(
        "-i",
        "--image",
        required=True,
        help="path to input image",
    )
    ap.add_argument(
        "-f",
        "--face",
        type=str,
        default="face_detector",
        help="path to face detector model directory",
    )
    ap.add_argument(
        "-m",
        "--model",
        type=str,
        default="mask_detector.model",
        help="path to trained face mask detector model",
    )
    ap.add_argument(
        "-c",
        "--confidence",
        type=float,
        default=0.5,
        help="minimum probability to filter weak detections",
    )
    args = vars(ap.parse_args())

    # load our serialized face detector model from disk
    print("[INFO] loading face detector model...")
    prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"])
    weightsPath = os.path.sep.join(
        [
            args["face"],
            "res10_300x300_ssd_iter_140000.caffemodel",
        ],
    )
    net = cv2.dnn.readNet(prototxtPath, weightsPath)

    # load the face mask detector model from disk
    print("[INFO] loading face mask detector model...")
    model = load_model(args["model"])

    # load the input image from disk, clone it, and grab the image spatial
    # dimensions
    image = cv2.imread(args["image"])
    (h, w) = image.shape[:2]

    # construct a blob from the image
    blob = cv2.dnn.blobFromImage(
        image,
        1.0,
        (300, 300),
        (104.0, 177.0, 123.0),
    )

    # pass the blob through the network and obtain the face detections
    print("[INFO] computing face detections...")
    net.setInput(blob)
    detections = net.forward()

    # loop over the detections
    for i in range(0, detections.shape[2]):
        # extract the confidence (i.e., probability) associated with
        # the detection
        confidence = detections[0, 0, i, 2]

        # filter out weak detections by ensuring the confidence is
        # greater than the minimum confidence
        if confidence > args["confidence"]:
            # compute the (x, y)-coordinates of the bounding box for
            # the object
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # ensure the bounding boxes fall within the dimensions of
            # the frame
            (startX, startY) = (max(0, startX), max(0, startY))
            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

            # extract the face ROI, convert it from BGR to RGB channel
            # ordering, resize it to 224x224, and preprocess it
            face = image[startY:endY, startX:endX]
            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
            face = cv2.resize(face, (224, 224))
            face = img_to_array(face)
            face = preprocess_input(face)
            face = np.expand_dims(face, axis=0)

            # pass the face through the model to determine if the face
            # has a mask or not
            (mask, withoutMask) = model.predict(face)[0]

            # determine the class label and color we'll use to draw
            # the bounding box and text
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (0, 0, 255)

            # include the probability in the label
            label = f"{label}: {max(mask, withoutMask) * 100:.2f}%"

            # display the label and bounding box rectangle on the output
            # frame
            cv2.putText(
                image,
                label,
                (startX, startY - 10),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.45,
                color,
                2,
            )
            cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)

    # show the output image
    cv2.imshow("Output", image)
    cv2.waitKey(0)


def detect_mask_in_image(image, faceNet, maskNet):
    # dimensions
    (h, w) = image.shape[:2]

    # construct a blob from the image
    blob = cv2.dnn.blobFromImage(
        image,
        1.0,
        (300, 300),
        (104.0, 177.0, 123.0),
    )  # TODO: add to config

    # pass the blob through the network and obtain the face detections
    print("[INFO] computing face detections...")
    faceNet.setInput(blob)
    detections = faceNet.forward()

    face_count = 0
    # image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    # loop over the detections
    for i in range(0, detections.shape[2]):
        # extract the confidence associated with the detection
        confidence = detections[0, 0, i, 2]
        # print(f"[INFO] face {i}: {confidence}")
        # filter out weak detections by ensuring the confidence is
        # greater than the minimum confidence
        if confidence > 0.5:
            face_count += 1
            # compute the (x, y)-coordinates of the object's bbox
            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
            (startX, startY, endX, endY) = box.astype("int")

            # ensure the bounding boxes fall within the dimensions of
            # the frame
            (startX, startY) = (max(0, startX), max(0, startY))
            (endX, endY) = (min(w - 1, endX), min(h - 1, endY))

            # extract the face ROI
            # ordering, resize it to 224x224, and preprocess it
            face = image[startY:endY, startX:endX]
            face = cv2.cvtColor(face, cv2.COLOR_BGR2RGB)
            face = cv2.resize(face, (224, 224))
            face = img_to_array(face)
            face = preprocess_input(face)
            face = np.expand_dims(face, axis=0)

            # pass the face through the model to determine if the face
            # has a mask or not
            (mask, withoutMask) = maskNet.predict(face)[0]

            # determine the class label and color we'll use to draw
            # the bounding box and text
            label = "Mask" if mask > withoutMask else "No Mask"
            color = (0, 255, 0) if label == "Mask" else (255, 0, 0)

            # include the probability in the label
            label = f"{label}: {max(mask, withoutMask) * 100:.2f}%"

            # display the label & bbox rectangle on the output frame
            cv2.putText(
                image,
                label,
                (startX, startY - 10),
                cv2.FONT_HERSHEY_SIMPLEX,
                0.45,
                color,
                2,
            )
            cv2.rectangle(image, (startX, startY), (endX, endY), color, 2)
        else:
            break
    text = f"[INFO] Detect {face_count} face(s)."
    print(text)
    cv2.putText(
        image,
        text,
        (10, 30),
        cv2.FONT_HERSHEY_SIMPLEX,
        0.70,
        (0, 255, 0),
        2,
    )
    return image


if __name__ == "__main__":
    mask_image()