# URL: https://huggingface.co/spaces/gradio/image_segmentation/
# DESCRIPTION: Image segmentation using DETR. Takes in both an inputu image and the desired confidence, and returns a segmented image.
# imports
import gradio as gr
import torch
import random
import numpy as np
from transformers import MaskFormerFeatureExtractor, MaskFormerForInstanceSegmentation


# load model
device = torch.device("cpu")
model = MaskFormerForInstanceSegmentation.from_pretrained("facebook/maskformer-swin-tiny-ade").to(device)
model.eval()
preprocessor = MaskFormerFeatureExtractor.from_pretrained("facebook/maskformer-swin-tiny-ade")

# define core and helper fns
def visualize_instance_seg_mask(mask):
    image = np.zeros((mask.shape[0], mask.shape[1], 3))
    labels = np.unique(mask)
    label2color = {label: (random.randint(0, 1), random.randint(0, 255), random.randint(0, 255)) for label in labels}
    for i in range(image.shape[0]):
      for j in range(image.shape[1]):
        image[i, j, :] = label2color[mask[i, j]]
    image = image / 255
    return image

def query_image(img):
    target_size = (img.shape[0], img.shape[1])
    inputs = preprocessor(images=img, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    outputs.class_queries_logits = outputs.class_queries_logits.cpu()
    outputs.masks_queries_logits = outputs.masks_queries_logits.cpu()
    results = preprocessor.post_process_segmentation(outputs=outputs, target_size=target_size)[0].cpu().detach()
    results = torch.argmax(results, dim=0).numpy()
    results = visualize_instance_seg_mask(results)
    return results

# define interface 

demo = gr.Interface(
    query_image, 
    inputs=[gr.Image()], 
    outputs="image",
    title="MaskFormer Demo",
    examples=[["example_2.png"]]
)

# launch 
demo.launch()