File size: 1,198 Bytes
4578a60 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 | import numpy as np
import cv2 as cv2
from tensorflow import keras
import gradio as gr
import matplotlib.pyplot as plt
import os
model = keras.models.load_model('./model1.h5')
def pipeline(img_path, model= model):
img = plt.imread(img_path)
width = 96
height = 96
dim = (width, height)
resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)
resized_gray = cv2.cvtColor(resized, cv2.COLOR_BGR2GRAY)
formato = resized_gray.reshape(96,96,1)
formato = np.repeat(formato, 3, axis=2)
formato = np.expand_dims(formato,0)
puntos = model.predict(formato)
plt.imshow(resized)
for i in range(1,31,2):
plt.plot(puntos[0][i-1], puntos[0][i], 'ro')
plt.savefig('tran.jpg')
img = cv2.imread('tran.jpg')
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
os.remove('tran.jpg')
plt.clf()
return img
examples=[]
examples.append("./1.png")
examples.append("./2.png")
examples.append("./3.png")
examples.append("./4.png")
examples.append("./5.png")
gr.Interface(
pipeline,
inputs=gr.inputs.Image(label="Upload THE FACEEEEOOOO", type="filepath"),
outputs=gr.outputs.Image(type="numpy"),
title="point on your uglo face >:(",
examples=examples
).launch() |