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import gradio as gr
import tensorflow as tf
import cv2
import mtcnn
import numpy as np
model = tf.keras.models.load_model('./model')
def load_and_preprocess_image(im_path, detector, maxWidth = 512):
desiredLeftEye = (0.36, 0.43)
# Load the image and convert it to grayscale
try:
image = cv2.imread(im_path)
except:
return 0
if image is None:
return 0
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# Detect the face in the image
result = detector.detect_faces(image)
# Get the bounding box for the face
x, y, w, h = result[0]['box']
desiredFaceWidth = 224
desiredFaceHeight = 224
# Get the landmarks for the face
landmarks = result[0]['keypoints']
# Calculate the angle between the eyes
eye_1 = landmarks['left_eye']
eye_2 = landmarks['right_eye']
dy = eye_2[1] - eye_1[1]
dx = eye_2[0] - eye_1[0]
angle = np.arctan2(dy, dx) * 180 / np.pi
desiredRightEyeX = 1.0 - desiredLeftEye[0]
dist = np.sqrt((dx ** 2) + (dy ** 2))
desiredDist = (desiredRightEyeX - desiredLeftEye[0]) * desiredFaceWidth
scale = desiredDist / dist
eyesCenter = ((eye_1[0] + eye_2[0]) // 2, (eye_1[1] + eye_2[1]) // 2)
# grab the rotation matrix for rotating and scaling the face
M = cv2.getRotationMatrix2D(eyesCenter, angle, scale)
# update the translation component of the matrix
tX = desiredFaceWidth * 0.5
tY = desiredFaceHeight * desiredLeftEye[1]
M[0, 2] += (tX - eyesCenter[0])
M[1, 2] += (tY - eyesCenter[1])
(w, h) = (desiredFaceWidth, desiredFaceHeight)
output = cv2.warpAffine(image, M, (w, h), flags=cv2.INTER_CUBIC)
output = np.array(output)
output = tf.image.convert_image_dtype(output, dtype=tf.float32)
output = tf.image.rgb_to_grayscale(output)
output = tf.tile(output, [1, 1, 3])
output = tf.clip_by_value(output, 0, 1)
return output
def predict_remaining_life(img_path):
detector = mtcnn.MTCNN()
# Transform the target image and add a batch dimension
img = load_and_preprocess_image(img_path, detector)
img = np.expand_dims(img, axis = 0)
#print(img.shape)
#plt.imshow(img)
# Put model into evaluation mode and turn on inference mode
pred = model.predict(img)
pred = round(pred[0][0]*100,1)
# Return the prediction dictionary and prediction time
return pred
# Create title, description and article strings
title = "Remaining Life Predictor"
description = "A Convolutional Neural Net to predict how many years a person has left to live using their facial image"
article = "Methodology and data explained at [arxiv article](https://arxiv.org/abs/2301.08229)"
# Create the Gradio demo
demo = gr.Interface(fn=predict_remaining_life, # mapping function from input to output
inputs=gr.Image(type="filepath"), # what are the inputs?
outputs=gr.Number(label="Remaining Life (Year)"),
title=title,
description=description,
article=article)
# Launch the demo!
demo.launch(debug=False, # print errors locally?
share=False) # generate a publically shareable URL? |