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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+mask_detector.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+import tensorflow as tf
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+import streamlit as st
+from PIL import Image
+
+st.image(r"face-mask.png", width = 150)
+st.write("# Mask detector App")
+
+# load model
+@st.cache_resource
+def cache_model(model_add):
+    model = tf.keras.models.load_model(model_add)
+    return model
+
+model = cache_model("mask_detector.keras")
+
+labels = ['WithMask', 'WithoutMask']
+def predict(img,model,labels = labels):
+  # read_image
+#   img = cv2.imread(img_add)
+  # resize image to 224, 224
+  img = cv2.resize(img, (224,224))
+  # st.write("resize_cv2", img.shape)
+  # convert to RGB
+  img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
+  img_expand = tf.expand_dims(img_rgb, axis = 0)
+  y_pred_prob = model.predict(img_expand)
+  y_pred_prob_round = np.round(y_pred_prob[0][0], 3)
+  st.write(y_pred_prob[0][0])
+  y_pred = int(np.round(y_pred_prob_round))
+  label = labels[y_pred]
+  return (label, y_pred_prob_round)
+
+def face_detect(img, model, labels = labels):
+#   img = cv2.imread(img_add)
+  face_detector=cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+  img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+  faces = face_detector.detectMultiScale(img_gray)
+  if len(faces) > 0:
+    for face in faces:
+      x,y,w,h = face
+      x_max = x + w
+      y_max = y + h
+      face_crp = img[y:y_max+1, x:x_max+1]
+      label, prob = predict(face_crp,model,labels = labels)
+      if label == labels[0]:
+        cv2.rectangle(img, (x,y), (x_max, y_max), (0,255,0), 3)
+        cv2.putText(img, f"{label} -: {1 - prob}%", (x,y-10), cv2.FONT_HERSHEY_SIMPLEX,
+                     1, (0,255,0), 2)
+      else:
+        cv2.rectangle(img, (x,y), (x_max, y_max), (255,0,0), 3)
+        cv2.putText(img, f"{label} -: {prob}%", (x,y-10), cv2.FONT_HERSHEY_SIMPLEX,
+                     1, (255,0,0), 2)
+    return img
+
+
+# File uploader
+uploaded_file = st.file_uploader("upload image", type=["jpg", "jpeg", "png"])
+submit_btn = st.button("Submit")
+
+if submit_btn:
+  if uploaded_file:
+    img_array = np.array(Image.open(uploaded_file))
+    # st.write(img_array.shape)
+    result_img = face_detect(img_array, model)
+    # result_img_rgb = cv2.cvtColor(result_img, cv2.COLOR_BGR2RGB)
+    st.image(result_img)
+  else:
+    st.write("Please upload an image")

mask_detector.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:be03cad9db624364f78da80d545ca3fdaba751c25d6fe5113f3f2a9ecd5fa50f
+size 23988034

requirements.txt

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+streamlit == 1.36.0
+numpy == 1.26.4
+opencv-python == 4.10.0.84
+tensorflow == 2.16.1