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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):
if image is None:
image = Image.open(config['BANNER_IMG'])
return image, "NO FACE DETECTED", "NO FACE DETECTED"
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 original_image , "NO FACE DETECTED", "NO FACE DETECTED"
x, y, w, h = faces[0]
image = Image.fromarray(image)
image = image.crop((x, y, x + w, y + h))
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)
return result_image, f"~ {(data['age'])}", data['gender']
# initializing the input component
image = gr.inputs.Image()
# initializzing examples
test_imgs = [os.path.join(config['TEST_IMG_PATH'],img) for img in os.listdir(config['TEST_IMG_PATH'])]
examples = test_imgs
# launching the interface
gr.Interface(fn = main_pipeline, title= 'Age & Gender predictions', inputs = image,
outputs = [gr.Image(label= 'Result', shape= (250,250)),
gr.Text(label= 'Age'),
gr.Text(label= 'Gender')], flagging_options=["correct", "incorrect", "other"],
examples= examples, capture_session = True).launch() |