Alex Gonzalez
Facial Recognition Before Prediction
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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()