mp.py

import PIL.Image
import cv2
import mediapipe as mp
# from mediapipe.python.solutions.drawing_utils import *
import tensorflow as tf
from fastapi import FastAPI, File, UploadFile
from PIL import Image, ImageOps


mp_face_detection = mp.solutions.face_detection
mp_drawing = mp.solutions.drawing_utils
model = tf.keras.models.load_model('mask_mobile_net.h5')
# print(model.summary())
FONT = cv2.FONT_HERSHEY_SIMPLEX


# creating an instance of FastAPI
app = FastAPI()
#
# @app.get("/image")
# def image():

# For static images:
IMAGE_FILES = []

with mp_face_detection.FaceDetection(
    model_selection=1,
    min_detection_confidence=0.5,
) as face_detection:

  for idx, file in enumerate(IMAGE_FILES):
    image = cv2.imread(file)
    # Convert the BGR image to RGB and process it with MediaPipe Face Detection.
    results = face_detection.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))

    # Draw face detections of each face.
    if not results.detections:
      continue

    annotated_image = image.copy()

    for detection in results.detections:
      print('Nose tip:')
      print(mp_face_detection.get_key_point(
          detection, mp_face_detection.FaceKeyPoint.NOSE_TIP))
      mp_drawing.draw_detection(annotated_image, detection)
    cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)


@app.post("/video")
def video():
    # For webcam input:
    return

cap = cv2.VideoCapture(0)
with mp_face_detection.FaceDetection(model_selection=0, min_detection_confidence=0.5) as face_detection:
  while cap.isOpened():
    success, image = cap.read()
    if not success:
      print("Ignoring empty camera frame.")
      # If loading a video, use 'break' instead of 'continue'.
      continue

    # To improve performance, optionally mark the image as not writeable to
    # pass by reference.
    image.flags.writeable = False
    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    results = face_detection.process(image)

    # Draw the face detection annotations on the image.
    image.flags.writeable = True
    image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

    if results.detections:

      for detection in results.detections:

        # points for the bouding box of the detected face
        x = int(detection.location_data.relative_bounding_box.xmin * 640)
        y = int(detection.location_data.relative_bounding_box.ymin * 480)
        w = int(detection.location_data.relative_bounding_box.width * 640)
        h = int(detection.location_data.relative_bounding_box.height * 480)

        # declaring the face dimensions
        face = image[y:y + h, x:x + w, :]
        face = cv2.resize(image, (224, 224))
        face = tf.keras.preprocessing.image.img_to_array(face)
        face = face / 255.
        face = tf.expand_dims(face, axis=0)

        pred = model.predict(face)
        pred_res = pred.argmax(axis=1)[0]

        print(pred)
        print(pred_res)

        label = "WITH MASK" if pred_res == 0 else "NO MASK"
        color = (0, 255, 0) if label == "WITH MASK" else (0, 0, 255)

        # label = "{}: {:.2f}%".format(label, max(pred) * 100)

        cv2.rectangle(image, pt1=(x, y), pt2=(x + w, y + h), color=color, thickness=2)
        cv2.putText(image, label, (x, y - 10), FONT, 0.5, color, 2)


    # Flip the image horizontally for a selfie-view display.
    cv2.imshow('Face Mask Detector', image)



    if cv2.waitKey(5) & 0xFF == 27:
      break
cap.release()