Added app.py

app.py

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+import gradio as gr
+import keras
+import cv2
+from tensorflow.keras.preprocessing.image import img_to_array
+from tensorflow.keras.preprocessing.image import load_img
+from tensorflow.keras.applications.mobilenet_v2 import preprocess_input
+import numpy as np
+from PIL import Image
+
+def modelpred(img):
+  #Loading the saved model
+  frame=cv2.imread(img)
+  gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+  faces = faceCascade.detectMultiScale(gray,scaleFactor=1.1,minNeighbors=5,minSize=(60, 60),flags=cv2.CASCADE_SCALE_IMAGE)
+  faces_list=[]
+  preds=[]
+  locs=[]
+  label_list=[]
+  for (x, y, w, h) in faces:
+    face_frame = frame[y:y+h,x:x+w]
+    face_frame = cv2.cvtColor(face_frame, cv2.COLOR_BGR2RGB)
+    face_frame = cv2.resize(face_frame, (224, 224))
+    face_frame = img_to_array(face_frame)
+    face_frame = np.expand_dims(face_frame, axis=0)
+    face_frame =  preprocess_input(face_frame)
+    (startX, startY, endX, endY) = (x,y,x+w,y+h)
+    box=(startX, startY, endX, endY)
+    locs.append(box)
+    p=model.predict(face_frame)
+    preds.append(p)
+      
+  for (box,pred) in zip(locs,preds):
+    (startX, startY, endX, endY) = box
+    (withoutMask,mask,notproper) = pred[0]
+    # print(pred)
+      
+    # determine the class label and color we'll use to draw
+    # the bounding box and text
+    if (mask > withoutMask and mask>notproper):
+      label = "Great you have worn the mask correctly"
+    elif ( withoutMask > notproper):
+      label = "Please wear a mask"
+    else:
+      label = "Please wear the mask properly"
+
+    if label == "Great you have worn the mask correctly":
+      color = (0, 255, 0)
+    elif label=="Please wear a mask":
+      color = (0, 0, 255)
+    else:
+      color = (255, 140, 0)
+
+
+          # include the probability in the label
+    label = "{}: {:.2f}%".format(label,max(mask, withoutMask, notproper) * 100)
+    label_list.append(label)
+          # display the label and bounding box rectangle on the output
+          # frame
+    cv2.putText(frame, label, (startX, startY - 10),cv2.FONT_HERSHEY_SIMPLEX, 0.45, color, 2)
+    cv2.rectangle(frame, (startX, startY), (endX, endY), color, 2)
+      
+# Display the resulting frame
+  
+# You may need to convert the color.
+  img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+  im_pil = Image.fromarray(img)
+  return (im_pil,label_list)
+imgpath=gr.inputs.Image(type="filepath")
+# webcam=gr.inputs.Image(source="webcam",type="filepath",optional=True)
+iface = gr.Interface(fn=modelpred, inputs=imgpath, outputs=[gr.outputs.Image(type="pil"),"text"],title="Face Mask Detection using Deep Neural Networks",description="""Implementation of an efficient neural network to detect and differentiate between people with high accuracy into 3 classes - those who have correctly worn face masks, those who have worn masks incorrectly and those who have not worn them. Implemented and fine tuned a MobileNetV2 network for this task and achieved an accuracy of 92.02%.""",allow_flagging="never",live=False,examples=[["images-3.jpeg"],["power-family-with-father-mother-daughter-wearing-medical-face-mask-protect-2019-ncov-covid-19-corona-virus-stay-home-concept_73622-1419.jpg"],["3000-2.jpg"]])
+model = keras.models.load_model('FMf5model-062.model')
+faceCascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_alt2.xml')
+iface.launch(debug=True)