detect_mask_image.py

# import the necessary packages
from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
from tensorflow.keras.preprocessing.image import img_to_array
from tensorflow.keras.models import load_model
import numpy as np
import cv2
import os

# load our serialized face detector model from disk

def detect(image,face="face_detector",model="mask_detector.model",cnf=0.5):
    prototxtPath = os.path.sep.join([face, "deploy.prototxt"])
    weightsPath = os.path.sep.join([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(model)
    # load the input image from disk, clone it, and grab the image spatial
    # dimensions
    image = cv2.imread(image)
    orig = image.copy()
    (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 > cnf:
            # 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
            try:
                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 = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100)
                # 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)
            except:pass
            
    # show the output image
    return image


if __name__=="__main__":
    out=detect(image="examples/1.jpg")
    cv2.imshow("output",out)
    print("[INFO] showing output, check image ...")
    cv2.waitKey(0)