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.gitignore

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+maskVenv
+# /assets/dataset

README.md

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----
-title: Real Time Face Mask Detector
-emoji: 🦀
-colorFrom: indigo
-colorTo: blue
-sdk: gradio
-sdk_version: 4.36.1
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Mask Detector
+emoji: ⚡
+colorFrom: pink
+colorTo: green
+sdk: gradio
+sdk_version: 4.36.1
+app_file: app.py
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+

app.py

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+# import the necessary packages
+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
+from imutils.video import VideoStream
+import numpy as np
+import imutils
+import time
+import cv2
+import os
+
+import gradio as gr
+
+# load our serialized face detector model from disk
+prototxtPath = r"assets/model/deploy.prototxt.txt"
+weightsPath = r"assets/model/res10_300x300_ssd_iter_140000.caffemodel"
+faceNet = cv2.dnn.readNet(prototxtPath,weightsPath)
+
+# load the face mask detector model from disk
+maskNet = load_model("assets/model/mask_detector.keras")
+
+
+def detect_and_predict_mask(frame, faceNet, maskNet):
+    try:
+        # grab the dimensions of the frame and then construct a blob from it
+        (h, w) = frame.shape[:2]
+        blob = cv2.dnn.blobFromImage(frame, 1.0, (224,224),(104.0,177.0,123.0) )
+
+        # pass the blob through the network and obtain the face detections
+        faceNet.setInput(blob)
+        detections = faceNet.forward()
+        print(detections.shape)
+
+        # initialize our list of faces, their corresponding locations, and the list of predictions from our face mask network
+        faces = []
+        locs = []
+        preds = []
+        # loop over the detections
+        for i in range(0,detections.shape[2]):
+            # extract the confidence (i.e., probability) associated with the detection
+            confidence = detections[0,0,i,2]
+
+            # filter out weak detections by ensuring the confidence is greater than minimum confidence
+            if confidence > 0.5:
+                # compute the (x, y)-cordinates of the bounding box for the object
+                box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+                (startX, startY, endX, endY) = box.astype("int")
+
+                # ensure the bounding boxes fall within the dimensions of the frame
+                (startX , startY) = (max(0,startX) , max(0,startY))
+                (endX, endY) = (min(w-1,endX) , min(h-1,endY))
+
+                # extract the face ROI, convert it from BGR to RGB channel ordering, resize it to 224x224, and preprocess it face=frame[startY:endY, startX:endX]
+                # bounding mask only for face detected
+                face = frame[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)
+
+                # add the face and bounding boxes to their respective lists
+                faces.append(face)
+                locs.append((startX, startY, endX, endY))
+
+        # only make a predictions if at least one face was detected
+        if len(faces) > 0:
+            # far faster inference we'll make batch predictions on *all* faces at the same time rather than one-by-one predictions in the above 'for' loop
+            faces = np.array(faces,dtype="float32")
+            preds = maskNet.predict(faces, batch_size=32)
+
+        # return a 2-tuple of the face locations and their corresponding locations
+        return (locs, preds)
+    except Exception as e:
+        print(e)
+
+def webcam_stream(frame):
+    if type(frame)==type(None):
+        return
+    while True:
+        try:
+            # grab the frame from the threaded video stream and resize it to have a max width of 400 pixels
+            frame = imutils.resize(frame,width=400)
+
+            # detect faces in the frame and determine if they are wearing a face mask or not
+            (locs, preds) = detect_and_predict_mask(frame, faceNet, maskNet)
+
+            # loop over the detected face locations and their correspondings locations
+            for (box, pred) in zip(locs, preds):
+                # unpack the bounding box and predictions
+                (startX, startY, endX, endY) = box
+                (mask, withoutMask) = pred
+
+                # determine the class label and color we'll use to draw the bounding box and text
+                label = "Mask" if mask> withoutMask else "No Mask"
+                color = (0,255,0) if label=="Mask" else (0,0,255)
+
+                # include the probability in the label
+                label = "{}: {:.2f}%".format(label,max(mask, withoutMask) *100)
+
+                # 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)
+        
+
+            # show the output frame
+            # cv2.imshow("Frame",frame)
+            # key = cv2.waitKey(1) & 0xFF
+
+            # if the 'q' key was pressed, break from the loop
+            # if key == ord("q"):
+            #     break
+        except Exception as e:
+            print(e)
+            
+        return frame
+# do a bit of cleanup
+# cv2.destroyAllWindows()
+
+
+
+webcam = gr.Image(sources=["webcam"],streaming=True,every="float",mirror_webcam=True)
+output = gr.Image(sources=["webcam"])
+# Create a Gradio interface with the webcam_stream function
+app = gr.Interface(webcam_stream,inputs=webcam,outputs=output,live=True)
+
+# Start the app
+app.launch() 
+gr.close_all()

assets/Mask_detector.mp4

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