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| import json | |
| import os | |
| from fastapi import FastAPI, Request | |
| from matplotlib import pyplot as plt | |
| from PIL import Image | |
| from fastapi.middleware.cors import CORSMiddleware | |
| import tensorflow as tf | |
| import numpy as np | |
| import yaml | |
| import io | |
| import cv2 | |
| # read config file | |
| def read_config(): | |
| config = {} | |
| print(os.path.curdir) | |
| with open(os.path.join('api','models_config.yaml'), 'r') as cf: | |
| config = yaml.safe_load(cf) | |
| for var in config: | |
| config[var] = config[var].replace(';', os.sep) | |
| return config | |
| # create app and load model | |
| config = read_config() | |
| service = FastAPI() | |
| # read path to test images | |
| test_img = [os.path.join(config['TEST_IMG_PATH'], 'test1.jpg'), os.path.join(config['TEST_IMG_PATH'], 'test2.jpg'), os.path.join(config['TEST_IMG_PATH'], 'test3.jpg'), | |
| os.path.join(config['TEST_IMG_PATH'], 'test4.jpg'), os.path.join(config['TEST_IMG_PATH'], 'test5.jpg'), os.path.join(config['TEST_IMG_PATH'], 'test6.jpg'), | |
| os.path.join(config['TEST_IMG_PATH'], 'test7.jpg')] | |
| # load pretrained models | |
| age_model = tf.keras.models.load_model(config['A_M_PATH']) | |
| gender_model = tf.keras.models.load_model(config['G_M_PATH']) | |
| face_cascade = cv2.CascadeClassifier(config['FD_M_PATH']) | |
| # add CORS middleware | |
| service.add_middleware( | |
| CORSMiddleware, | |
| allow_origins=['*'] | |
| ) | |
| # status route | |
| async def read_root(): | |
| """ | |
| Check status of routes | |
| """ | |
| url_list = [{"path": route.path, "name": route.name} for route in service.routes] | |
| if len(url_list) == 7: | |
| return "Healthy" | |
| else: | |
| return "Unhealthy" | |
| async def make_predictions_pipeline(request, from_slider= False): | |
| if from_slider: | |
| file = await request.form() | |
| img_ind = file['img'] | |
| image = Image.open(test_img[int(img_ind)]) | |
| else: | |
| file = await request.form() | |
| im_b64 = file['img'] | |
| image = im_b64.file.read() | |
| image = Image.open(io.BytesIO(image)) | |
| image = image.convert("RGB") | |
| image = np.asarray(image) | |
| gray_img = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY) | |
| faces = face_cascade.detectMultiScale(gray_img, 1.1, 4) | |
| if len(faces) == 0: | |
| return "NO FACE DETECTED" | |
| x, y, w, h = faces[0] | |
| #cv2.rectangle(image, (x, y), (x+w, y+h), (255, 0, 0), 2) | |
| image = Image.fromarray(image) | |
| image = image.crop((x, y, x + w, y + h)) | |
| #cv2.imshow('image', np.asarray(image)) | |
| #cv2.waitKey() | |
| image = tf.image.resize(image, [224,224]) | |
| image = tf.keras.preprocessing.image.img_to_array(image) | |
| image = image / 255.0 | |
| image = tf.expand_dims(image, axis=0) | |
| age_prds = age_model.predict(image) | |
| gender_prds = gender_model.predict(image) | |
| age_prds = np.around(age_prds) | |
| gender_prds = np.around(gender_prds) | |
| gender = "" | |
| if gender_prds[0][0] == 0: | |
| gender = 'male' | |
| else: | |
| gender = 'female' | |
| data = {} | |
| data['age'] = str(age_prds[0][0]) | |
| data['gender'] = gender | |
| b_box_arr = [(x, y, w, h) for (x, y, w, h) in faces] | |
| data['left'] = str(b_box_arr[0][0]) | |
| data['top'] = str(b_box_arr[0][1]) | |
| data['width'] = str(b_box_arr[0][2]) | |
| data['height'] = str(b_box_arr[0][3]) | |
| predictions_json = json.dumps(data) | |
| return predictions_json | |
| # slider predictions route | |
| async def receive_image(request: Request): | |
| """ | |
| Function for predicts from slider | |
| """ | |
| results_json = await make_predictions_pipeline(request, from_slider= True) | |
| return results_json | |
| # main predictions route | |
| async def receive_image(request: Request): | |
| """ | |
| Function for predicts | |
| Example request: | |
| img = open( FILEPATH , 'rb') | |
| files = {'img': img} | |
| resp = requests.post("http://{host:port}/api/predictions", files= files) | |
| """ | |
| results_json = await make_predictions_pipeline(request, from_slider= False) | |
| return results_json |