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Configuration error
Configuration error
| import streamlit as st | |
| from PIL import Image, ImageEnhance | |
| import numpy as np | |
| import cv2 | |
| import os | |
| 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 detect_mask_image | |
| # Setting custom Page Title and Icon with changed layout and sidebar state | |
| st.set_page_config(page_title='Face Mask Detector', page_icon='😷', layout='centered', initial_sidebar_state='expanded') | |
| def local_css(file_name): | |
| """ Method for reading styles.css and applying necessary changes to HTML""" | |
| with open(file_name) as f: | |
| st.markdown(f'<style>{f.read()}</style>', unsafe_allow_html=True) | |
| def mask_image(): | |
| global RGB_img | |
| # load our serialized face detector model from disk | |
| print("[INFO] loading face detector model...") | |
| prototxtPath = os.path.sep.join(["face_detector", "deploy.prototxt"]) | |
| weightsPath = os.path.sep.join(["face_detector", | |
| "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("mask_detector.h5") | |
| # load the input image from disk and grab the image spatial | |
| # dimensions | |
| image = cv2.imread("./images/out.jpg") | |
| (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 > 0.5: | |
| # 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 = "{}: {:.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) | |
| RGB_img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) | |
| mask_image() | |
| def mask_detection(): | |
| local_css("css/styles.css") | |
| st.markdown('<h1 align="center">😷 Face Mask Detection</h1>', unsafe_allow_html=True) | |
| activities = ["Image", "Webcam"] | |
| #st.set_option('deprecation.showfileUploaderEncoding', False) | |
| st.sidebar.markdown("# Mask Detection on?") | |
| choice = st.sidebar.selectbox("Choose among the given options:", activities) | |
| if choice == 'Image': | |
| st.markdown('<h2 align="center">Detection on Image</h2>', unsafe_allow_html=True) | |
| st.markdown("### Upload your image here ⬇") | |
| image_file = st.file_uploader("", type=['jpg']) # upload image | |
| if image_file is not None: | |
| our_image = Image.open(image_file) # making compatible to PIL | |
| im = our_image.save('./images/out.jpg') | |
| saved_image = st.image(image_file, caption='', use_column_width=True) | |
| st.markdown('<h3 align="center">Image uploaded successfully!</h3>', unsafe_allow_html=True) | |
| if st.button('Process'): | |
| st.image(RGB_img, use_column_width=True) | |
| if choice == 'Webcam': | |
| st.markdown('<h2 align="center">Detection on Webcam</h2>', unsafe_allow_html=True) | |
| st.markdown('<h3 align="center">This feature will be available soon!</h3>', unsafe_allow_html=True) | |
| mask_detection() | |