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import gradio as gr
from fastai.vision.all import *
import ultralytics
from ultralytics import YOLO
from PIL import Image, ImageDraw, ImageFont
# import os
# Load a pre-trained image classification model
import pathlib
plt = platform.system()
if plt == 'Windows': pathlib.PosixPath = pathlib.WindowsPath
if plt == 'Linux': pathlib.WindowsPath = pathlib.PosixPath
root = os.path.dirname(__file__)
detect = YOLO('./models/detect.pt')
mood = load_learner("./models/mood.pkl")
# Function to make predictions from an image
def process(imagep):
boxes = detect.predict(imagep)
image = Image.open(imagep)
image_with_boxes = image.copy()
draw = ImageDraw.Draw(image_with_boxes)
for i, box in enumerate(boxes[0].boxes.xyxy):
# print(box)
x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(box[3])
cropped_image = image.crop((x1, y1, x2, y2))
resized_image = cropped_image.resize((48, 48))
grayscale_image = resized_image.convert('L')
w = 1 + (y2+x2-y1-x1)//50
pil_image = PILImage.create(grayscale_image)
prediction = mood.predict(pil_image)
# print(prediction)
text = prediction[0]
text_position = (x1 + w, y1 + w)
draw.rectangle([x1, y1, x2, y2], outline="red", width=w)
font = ImageFont.truetype("opensans.ttf", 5*w)
draw.text(text_position, text, fill="blue",font=font, stroke_width=int(w*0.2))
return image_with_boxes
# Sample images for user to choose from
sample_images = ["./sample_images/angry.jpg", "./sample_images/office.jpg","./sample_images/friends.jpg"]
iface = gr.Interface(
fn=process,
inputs=gr.Image(label="Select an image", type="filepath"),
outputs='image',
live=False,
title="Face Mood predictor",
description="Upload a facial image or select one of the examples below",
examples=sample_images
)
iface.launch() |