Upload app.py

app.py

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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)