Delete app.py.py

app.py.py

@@ -1,91 +0,0 @@
-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)