app.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
from imutils.video import VideoStream
import numpy as np
import imutils
import time
import cv2
import os

import gradio as gr

# load our serialized face detector model from disk
prototxtPath = r"assets/model/deploy.prototxt.txt"
weightsPath = r"assets/model/res10_300x300_ssd_iter_140000.caffemodel"
faceNet = cv2.dnn.readNet(prototxtPath,weightsPath)

# load the face mask detector model from disk
maskNet = load_model("assets/model/mask_detector.keras")


def detect_and_predict_mask(frame, faceNet, maskNet):
    try:
        # grab the dimensions of the frame and then construct a blob from it
        (h, w) = frame.shape[:2]
        blob = cv2.dnn.blobFromImage(frame, 1.0, (224,224),(104.0,177.0,123.0) )

        # pass the blob through the network and obtain the face detections
        faceNet.setInput(blob)
        detections = faceNet.forward()
        print(detections.shape)

        # initialize our list of faces, their corresponding locations, and the list of predictions from our face mask network
        faces = []
        locs = []
        preds = []
        # 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 minimum confidence
            if confidence > 0.5:
                # compute the (x, y)-cordinates 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=frame[startY:endY, startX:endX]
                # bounding mask only for face detected
                face = frame[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)

                # add the face and bounding boxes to their respective lists
                faces.append(face)
                locs.append((startX, startY, endX, endY))

        # only make a predictions if at least one face was detected
        if len(faces) > 0:
            # far faster inference we'll make batch predictions on *all* faces at the same time rather than one-by-one predictions in the above 'for' loop
            faces = np.array(faces,dtype="float32")
            preds = maskNet.predict(faces, batch_size=32)

        # return a 2-tuple of the face locations and their corresponding locations
        return (locs, preds)
    except Exception as e:
        print(e)

def webcam_stream(frame):
    if type(frame)==type(None):
        return
    while True:
        try:
            # grab the frame from the threaded video stream and resize it to have a max width of 400 pixels
            frame = imutils.resize(frame,width=400)

            # detect faces in the frame and determine if they are wearing a face mask or not
            (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)

            # loop over the detected face locations and their correspondings locations
            for (box, pred) in zip(locs, preds):
                # unpack the bounding box and predictions
                (startX, startY, endX, endY) = box
                (mask, withoutMask) = pred

                # 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(frame,label,(startX,startY-10), cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
                cv2.rectangle(frame, (startX,startY), (endX,endY),color,2)
        

            # show the output frame
            # cv2.imshow("Frame",frame)
            # key = cv2.waitKey(1) & 0xFF

            # if the 'q' key was pressed, break from the loop
            # if key == ord("q"):
            #     break
        except Exception as e:
            print(e)
            
        return frame
# do a bit of cleanup
# cv2.destroyAllWindows()



webcam = gr.Image(sources=["webcam"],streaming=True,every="float",mirror_webcam=True)
output = gr.Image(sources=["webcam"])
# Create a Gradio interface with the webcam_stream function
app = gr.Interface(webcam_stream,inputs=webcam,outputs=output,live=True)

# Start the app
app.launch() 
gr.close_all()