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| ''' | |
| Build Project | |
| ''' | |
| # imported necessary libraries | |
| import gradio as gr | |
| import tensorflow as tf | |
| import numpy as np | |
| import os | |
| import yaml | |
| import cv2 | |
| from PIL import Image | |
| import io | |
| import json | |
| # read config file | |
| def read_config(): | |
| config = {} | |
| print(os.path.curdir) | |
| with open('models_config.yaml', 'r') as cf: | |
| config = yaml.safe_load(cf) | |
| for var in config: | |
| config[var] = config[var].replace(';', os.sep) | |
| return config | |
| config = read_config() | |
| # loading the 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']) | |
| def main_pipeline(image): | |
| #file = request.form() | |
| #im_b64 = file['img'] | |
| #image = im_b64.file.read() | |
| #image = Image.open(image) | |
| #image = image.convert("RGB") | |
| original_image = image | |
| 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 = image.resize((224,224)) | |
| #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'] = int(age_prds[0][0]) | |
| data['gender'] = gender | |
| image_b_box = cv2.rectangle(np.asarray(original_image), (x, y), (x+w, y+h), (255, 0, 0), 2) | |
| result_image = Image.fromarray(image_b_box) | |
| result_image = result_image.resize((150,150)) | |
| return result_image, f"~ {(data['age'])}", data['gender'] | |
| # initializing the input component | |
| image = gr.inputs.Image(shape = (224, 224)) | |
| # initializing the output component | |
| labels = gr.outputs.Label() | |
| # launching the interface | |
| gr.Interface(fn = main_pipeline, title= 'Age & Gender predictions', inputs = image, | |
| outputs = [gr.Image(label= 'Result'), | |
| gr.Text(label= 'Age'), | |
| gr.Text(label= 'Gender')], flagging_options=["correct", "incorrect", "other"], capture_session = True).launch() |