"""
Using as reference:
- https://huggingface.co/nvidia/segformer-b0-finetuned-ade-512-512
- https://huggingface.co/spaces/chansung/segformer-tf-transformers/blob/main/app.py
- https://huggingface.co/facebook/detr-resnet-50-panoptic
"""

from transformers import DetrFeatureExtractor, DetrForSegmentation
from PIL import Image
import gradio as gr
import numpy as np

# Returns a list with a color per ADE class (150 classes)
# from https://huggingface.co/spaces/chansung/segformer-tf-transformers/blob/main/app.py
def ade_palette():
    """ADE20K palette that maps each class to RGB values."""
    return [
        [120, 120, 120],
        [180, 120, 120],
        [6, 230, 230],
        [80, 50, 50],
        [4, 200, 3],
        [120, 120, 80],
        [140, 140, 140],
        [204, 5, 255],
        [230, 230, 230],
        [4, 250, 7],
        [224, 5, 255],
        [235, 255, 7],
        [150, 5, 61],
        [120, 120, 70],
        [8, 255, 51],
        [255, 6, 82],
        [143, 255, 140],
        [204, 255, 4],
        [255, 51, 7],
        [204, 70, 3],
        [0, 102, 200],
        [61, 230, 250],
        [255, 6, 51],
        [11, 102, 255],
        [255, 7, 71],
        [255, 9, 224],
        [9, 7, 230],
        [220, 220, 220],
        [255, 9, 92],
        [112, 9, 255],
        [8, 255, 214],
        [7, 255, 224],
        [255, 184, 6],
        [10, 255, 71],
        [255, 41, 10],
        [7, 255, 255],
        [224, 255, 8],
        [102, 8, 255],
        [255, 61, 6],
        [255, 194, 7],
        [255, 122, 8],
        [0, 255, 20],
        [255, 8, 41],
        [255, 5, 153],
        [6, 51, 255],
        [235, 12, 255],
        [160, 150, 20],
        [0, 163, 255],
        [140, 140, 140],
        [250, 10, 15],
        [20, 255, 0],
        [31, 255, 0],
        [255, 31, 0],
        [255, 224, 0],
        [153, 255, 0],
        [0, 0, 255],
        [255, 71, 0],
        [0, 235, 255],
        [0, 173, 255],
        [31, 0, 255],
        [11, 200, 200],
        [255, 82, 0],
        [0, 255, 245],
        [0, 61, 255],
        [0, 255, 112],
        [0, 255, 133],
        [255, 0, 0],
        [255, 163, 0],
        [255, 102, 0],
        [194, 255, 0],
        [0, 143, 255],
        [51, 255, 0],
        [0, 82, 255],
        [0, 255, 41],
        [0, 255, 173],
        [10, 0, 255],
        [173, 255, 0],
        [0, 255, 153],
        [255, 92, 0],
        [255, 0, 255],
        [255, 0, 245],
        [255, 0, 102],
        [255, 173, 0],
        [255, 0, 20],
        [255, 184, 184],
        [0, 31, 255],
        [0, 255, 61],
        [0, 71, 255],
        [255, 0, 204],
        [0, 255, 194],
        [0, 255, 82],
        [0, 10, 255],
        [0, 112, 255],
        [51, 0, 255],
        [0, 194, 255],
        [0, 122, 255],
        [0, 255, 163],
        [255, 153, 0],
        [0, 255, 10],
        [255, 112, 0],
        [143, 255, 0],
        [82, 0, 255],
        [163, 255, 0],
        [255, 235, 0],
        [8, 184, 170],
        [133, 0, 255],
        [0, 255, 92],
        [184, 0, 255],
        [255, 0, 31],
        [0, 184, 255],
        [0, 214, 255],
        [255, 0, 112],
        [92, 255, 0],
        [0, 224, 255],
        [112, 224, 255],
        [70, 184, 160],
        [163, 0, 255],
        [153, 0, 255],
        [71, 255, 0],
        [255, 0, 163],
        [255, 204, 0],
        [255, 0, 143],
        [0, 255, 235],
        [133, 255, 0],
        [255, 0, 235],
        [245, 0, 255],
        [255, 0, 122],
        [255, 245, 0],
        [10, 190, 212],
        [214, 255, 0],
        [0, 204, 255],
        [20, 0, 255],
        [255, 255, 0],
        [0, 153, 255],
        [0, 41, 255],
        [0, 255, 204],
        [41, 0, 255],
        [41, 255, 0],
        [173, 0, 255],
        [0, 245, 255],
        [71, 0, 255],
        [122, 0, 255],
        [0, 255, 184],
        [0, 92, 255],
        [184, 255, 0],
        [0, 133, 255],
        [255, 214, 0],
        [25, 194, 194],
        [102, 255, 0],
        [92, 0, 255],
    ]

feature_extractor = DetrFeatureExtractor.from_pretrained('facebook/detr-resnet-50-panoptic')
model = DetrForSegmentation.from_pretrained('facebook/detr-resnet-50-panoptic')

# gradio components
input = gr.inputs.Image()
output = gr.outputs.Image()

def predict_animal_mask(image):
    inputs = feature_extractor(images=image, return_tensors="pt") #pt=Pytorch, tf=TensorFlow
    outputs = model(**inputs)
    logits = outputs.logits 
    bboxes = outputs.pred_boxes
    masks = outputs.pred_masks

    # postprocess the image
    label_per_pixel = torch.argmax(masks.squeeze(),dim=0).detach().numpy()
    color_mask = np.zeros(image.size+(3,))
    for lbl, color in enumerate(ade_palette()):
        color_mask[label_per_pixel==lbl,:] = color

    # Show image + mask
    pred_img = np.array(image.convert('RGB'))*0.5 + color_mask*0.5
    pred_img = pred_img.astype(np.uint8)   


####################################################
# Create user interface and launch
gr.Interface(predict_animal_mask, 
    inputs = input, 
    outputs = output,
    title = 'Animals segmentation in images',
    description = "An animal segmentation image webapp using DETR (End-to-End Object Detection) model with ResNet-50 backbone").launch()


####################################
# url = "http://images.cocodataset.org/val2017/000000039769.jpg"
# image = Image.open(requests.get(url, stream=True).raw)

# inputs = feature_extractor(images=image, return_tensors="pt")
# outputs = model(**inputs)
# logits = outputs.logits  # shape (batch_size, num_labels, height/4, width/4)