app.py
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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
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.model")
# 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():
st.title("Face mask detection")
activities = ["Image", "Webcam"]
st.set_option('deprecation.showfileUploaderEncoding', False)
choice = st.sidebar.selectbox("Mask Detection on?", activities)
if choice == 'Image':
st.subheader("Detection on image")
image_file = st.file_uploader("Upload Image", 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='image uploaded successfully', use_column_width=True)
if st.button('Process'):
st.image(RGB_img, use_column_width=True)
if choice == 'Webcam':
st.subheader("Detection on webcam")
st.text("This feature will be avilable soon")
mask_detection()