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import streamlit as st
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 numpy as np
import cv2
import os
from tf_explain.core.grad_cam import GradCAM
from tf_explain.core.occlusion_sensitivity import OcclusionSensitivity
@st.cache(hash_funcs={cv2.dnn_Net: hash})
def load_face_detector_and_model():
prototxt_path = os.path.sep.join(["face_detector", "deploy.prototxt"])
weights_path = os.path.sep.join(["face_detector",
"res10_300x300_ssd_iter_140000.caffemodel"])
cnn_net = cv2.dnn.readNet(prototxt_path, weights_path)
return cnn_net
@st.cache(allow_output_mutation=True)
def load_cnn_model():
cnn_model = load_model("mask_detector.model")
return cnn_model
st.write('# Face Mask Image Detector')
net = load_face_detector_and_model()
model = load_cnn_model()
uploaded_image = st.sidebar.file_uploader("Choose a JPG file", type="jpg")
confidence_value = st.sidebar.slider('Confidence:', 0.0, 1.0, 0.5, 0.1)
if uploaded_image:
st.sidebar.info('Uploaded image:')
st.sidebar.image(uploaded_image, width=240)
grad_cam_button = st.sidebar.button('Grad CAM')
patch_size_value = st.sidebar.slider('Patch size:', 10, 90, 20, 10)
occlusion_sensitivity_button = st.sidebar.button('Occlusion Sensitivity')
image = cv2.imdecode(np.fromstring(uploaded_image.read(), np.uint8), 1)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
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_value:
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)
expanded_face = np.expand_dims(face, axis=0)
(mask, withoutMask) = model.predict(expanded_face)[0]
predicted_class = 0
label = "No Mask"
if mask > withoutMask:
label = "Mask"
predicted_class = 1
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)
st.image(image, width=640)
st.write('### ' + label)
if grad_cam_button:
data = ([face], None)
explainer = GradCAM()
grad_cam_grid = explainer.explain(
data, model, class_index=predicted_class, layer_name="Conv_1"
)
st.image(grad_cam_grid)
if occlusion_sensitivity_button:
data = ([face], None)
explainer = OcclusionSensitivity()
sensitivity_occlusion_grid = explainer.explain(data, model, predicted_class, patch_size_value)
st.image(sensitivity_occlusion_grid)
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