File size: 1,533 Bytes
16cf1ad af3e0f6 4fb5c0d 2a29d13 4fb5c0d 851ec4b 2a29d13 e34b118 4fb5c0d af3e0f6 851ec4b b34bbc3 851ec4b 4fb5c0d 16cf1ad 39d5445 e10e9b7 25e8fcd 16cf1ad 39d5445 |
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 |
import gradio as gr
from fastai.vision.all import *
from transformers import AutoImageProcessor, AutoModelForImageClassification
from PIL import Image
import requests
import face_recognition
learn_inf = load_learner("export.pkl")
processor = AutoImageProcessor.from_pretrained("dima806/facial_emotions_image_detection")
model = AutoModelForImageClassification.from_pretrained("dima806/facial_emotions_image_detection")
def extract_face(image)-> Image.Image:
# Detect face locations
face_locations = face_recognition.face_locations(image)
# If a face is detected, extract the first one
if face_locations:
top, right, bottom, left = face_locations[0]
face_image = Image.fromarray(image[top:bottom, left:right])
return face_image
else:
return image
def predict(value) -> str:
image = extract_face(Image.fromarray(value)).convert("L")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
return model.config.id2label[predicted_class_idx]
with gr.Blocks() as demo:
with gr.Row():
with gr.Column():
input_img = gr.Image(label="Input", sources="webcam")
with gr.Column():
output_lbl = gr.Label(value="Output", label="Expression Prediction")
input_img.stream(fn=predict, inputs=input_img, outputs=output_lbl, time_limit=15, stream_every=0.1, concurrency_limit=30)
if __name__ == "__main__":
demo.launch() |