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| import streamlit as st | |
| import numpy as np | |
| import cv2 | |
| 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 os | |
| # Function to load models | |
| def load_models(face_model_path, mask_model_path): | |
| # Load face detector model | |
| prototxtPath = os.path.sep.join([face_model_path, "deploy.prototxt"]) | |
| weightsPath = os.path.sep.join([face_model_path, "res10_300x300_ssd_iter_140000.caffemodel"]) | |
| net = cv2.dnn.readNet(prototxtPath, weightsPath) | |
| # Load face mask detector model | |
| model = load_model(mask_model_path) | |
| return net, model | |
| # Function to detect and display results | |
| def detect_mask(image, net, model, confidence_threshold=0.5): | |
| orig = image.copy() | |
| (h, w) = image.shape[:2] | |
| blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) | |
| net.setInput(blob) | |
| detections = net.forward() | |
| for i in range(0, detections.shape[2]): | |
| confidence = detections[0, 0, i, 2] | |
| if confidence > confidence_threshold: | |
| 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 = 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) | |
| (mask, withoutMask) = model.predict(face)[0] | |
| 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 | |
| # Streamlit app interface | |
| st.title("Face Mask Detection with TensorFlow") | |
| st.write("Upload an image to detect if people are wearing masks or not.") | |
| # Sidebar for model configurations | |
| face_model_dir = st.sidebar.text_input("Path to Face Detector Model", "face_detector") | |
| mask_model_path = st.sidebar.text_input("Path to Mask Detector Model", "mask_detector.model") | |
| confidence_threshold = st.sidebar.slider("Confidence Threshold", 0.1, 1.0, 0.5) | |
| # Load models | |
| net, model = load_models(face_model_dir, mask_model_path) | |
| # File uploader | |
| uploaded_file = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_file is not None: | |
| # Read image | |
| file_bytes = np.asarray(bytearray(uploaded_file.read()), dtype=np.uint8) | |
| image = cv2.imdecode(file_bytes, cv2.IMREAD_COLOR) | |
| # Detect mask | |
| result_image = detect_mask(image, net, model, confidence_threshold) | |
| # Convert result image to RGB for displaying | |
| result_image = cv2.cvtColor(result_image, cv2.COLOR_BGR2RGB) | |
| # Display images | |
| st.image(result_image, caption="Processed Image", use_column_width=True) | |