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| 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 argparse | |
| import imutils | |
| import time | |
| import cv2 | |
| import streamlit as st | |
| import urllib | |
| import gradio as gr | |
| def detect_and_predict_mask(frame, faceNet, maskNet): | |
| (h, w) = frame.shape[:2] | |
| blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), | |
| (104.0, 177.0, 123.0)) | |
| faceNet.setInput(blob) | |
| detections = faceNet.forward() | |
| faces = [] | |
| locs = [] | |
| preds = [] | |
| for i in range(0, detections.shape[2]): | |
| confidence = detections[0, 0, i, 2] | |
| if confidence > args["confidence"]: | |
| box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) | |
| (startX, startY, endX, endY) = box.astype("int") | |
| (startX, startY) = (max(0, startX), max(0, startY)) | |
| (endX, endY) = (min(w - 1, endX), min(h - 1, endY)) | |
| 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) | |
| face = np.expand_dims(face, axis=0) | |
| faces.append(face) | |
| locs.append((startX, startY, endX, endY)) | |
| if len(faces) > 0: | |
| preds = maskNet.predict(faces) | |
| return (locs, preds) | |
| ap = argparse.ArgumentParser() | |
| 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="model.h5", | |
| 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()) | |
| print("[INFO] loading face detector model...") | |
| # Define the URLs of the deploy.prototxt and caffe model files in the GitHub repo | |
| prototxt_url = "https://huggingface.co/spaces/Yogasta/Real_Time_Face_Mask_Detector/raw/main/deploy.prototxt" | |
| weights_url = "https://huggingface.co/spaces/Yogasta/Real_Time_Face_Mask_Detector/resolve/main/res10_300x300_ssd_iter_140000.caffemodel" | |
| # Define the local paths where the files will be downloaded | |
| prototxt_path = "deploy.prototxt" | |
| weights_path = "res10_300x300_ssd_iter_140000.caffemodel" | |
| # Download the files | |
| urllib.request.urlretrieve(prototxt_url, prototxt_path) | |
| urllib.request.urlretrieve(weights_url, weights_path) | |
| faceNet = cv2.dnn.readNet(prototxt_path, weights_path) | |
| print("[INFO] loading face mask detector model...") | |
| maskNet = load_model(args["model"]) | |
| print("[INFO] starting video stream...") | |
| # Streamlit initialization | |
| #st.title("Real-Time Face Mask Detection") | |
| #st.sidebar.title("Face Mask Detection") | |
| ## Select camera to feed the model | |
| #available_cameras = {'Camera 1': 0, 'Camera 2': 1, 'Camera 3': 2} | |
| #cam_id = st.sidebar.selectbox("Select which camera signal to use", list(available_cameras.keys())) | |
| # Define holder for the processed image | |
| #img_placeholder = st.empty() | |
| #vs = VideoStream(src=available_cameras[cam_id]).start() | |
| #time.sleep(2.0) | |
| startpred = st.button('Start Webcam') | |
| def process_image(image): | |
| (locs, preds) = detect_and_predict_mask(image, faceNet, maskNet) | |
| for (box, pred) in zip(locs, preds): | |
| (startX, startY, endX, endY) = box | |
| withoutMask = pred[0] | |
| mask = 1 - withoutMask | |
| label = "Mask" if mask > withoutMask else "No Mask" | |
| color = (0, 255, 0) if label == "Mask" else (0, 0, 255) | |
| label = "{}: {:.2f}%".format(label, max(mask, withoutMask) * 100) | |
| cv2.putText(image, label, (startX, startY - 10), | |
| cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2) | |
| cv2.rectangle(image, (startX, startY), (endX, endY), color, 2) | |
| return image | |
| iface = gr.Interface( | |
| fn=process_image, | |
| inputs=gr.inputs.Image(source="camera", tool="opencv", type="numpy"), | |
| outputs=gr.outputs.Image(type="numpy"), | |
| live=True, | |
| layout="vertical", | |
| title="Real-Time Face Mask Detection", | |
| description="A real-time face mask detection application using webcam input. Click 'Start' to activate the camera and see the detection results." | |
| ) | |
| iface.launch() |