from PIL import Image
import requests
import matplotlib.pyplot as plt
import torch
from torch import nn
from torchvision.models import resnet50
import torchvision.transforms as T
torch.set_grad_enabled(False);
import gradio as gr
import io

model = torch.hub.load('facebookresearch/detr', 'detr_resnet50', pretrained=True)

# Images
torch.hub.download_url_to_file('https://images.pexels.com/photos/461717/pexels-photo-461717.jpeg', 'horse.jpeg')
torch.hub.download_url_to_file('https://images.pexels.com/photos/5967799/pexels-photo-5967799.jpeg', 'turtle.jpeg')


# COCO classes
CLASSES = [
    'N/A', 'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus',
    'train', 'truck', 'boat', 'traffic light', 'fire hydrant', 'N/A',
    'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse',
    'sheep', 'cow', 'elephant', 'bear', 'zebra', 'giraffe', 'N/A', 'backpack',
    'umbrella', 'N/A', 'N/A', 'handbag', 'tie', 'suitcase', 'frisbee', 'skis',
    'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove',
    'skateboard', 'surfboard', 'tennis racket', 'bottle', 'N/A', 'wine glass',
    'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple', 'sandwich',
    'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake',
    'chair', 'couch', 'potted plant', 'bed', 'N/A', 'dining table', 'N/A',
    'N/A', 'toilet', 'N/A', 'tv', 'laptop', 'mouse', 'remote', 'keyboard',
    'cell phone', 'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'N/A',
    'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier',
    'toothbrush'
]

# colors for visualization
COLORS = [[0.000, 0.447, 0.741], [0.850, 0.325, 0.098], [0.929, 0.694, 0.125],
          [0.494, 0.184, 0.556], [0.466, 0.674, 0.188], [0.301, 0.745, 0.933]]

# standard PyTorch mean-std input image normalization
transform = T.Compose([
    T.Resize(800),
    T.ToTensor(),
    T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

# for output bounding box post-processing
def box_cxcywh_to_xyxy(x):
    x_c, y_c, w, h = x.unbind(1)
    b = [(x_c - 0.5 * w), (y_c - 0.5 * h),
         (x_c + 0.5 * w), (y_c + 0.5 * h)]
    return torch.stack(b, dim=1)

def rescale_bboxes(out_bbox, size):
    img_w, img_h = size
    b = box_cxcywh_to_xyxy(out_bbox)
    b = b * torch.tensor([img_w, img_h, img_w, img_h], dtype=torch.float32)
    return b

def fig2img(fig):
    """Convert a Matplotlib figure to a PIL Image and return it"""
    buf = io.BytesIO()
    fig.savefig(buf)
    buf.seek(0)
    return Image.open(buf)


def plot_results(pil_img, prob, boxes):
    plt.figure(figsize=(16,10))
    plt.imshow(pil_img)
    ax = plt.gca()
    colors = COLORS * 100
    for p, (xmin, ymin, xmax, ymax), c in zip(prob, boxes.tolist(), colors):
        ax.add_patch(plt.Rectangle((xmin, ymin), xmax - xmin, ymax - ymin,
                                   fill=False, color=c, linewidth=3))
        cl = p.argmax()
        text = f'{CLASSES[cl]}: {p[cl]:0.2f}'
        ax.text(xmin, ymin, text, fontsize=15,
                bbox=dict(facecolor='yellow', alpha=0.5))
    plt.axis('off')
    return fig2img(plt)
    


def detr(im):
  # mean-std normalize the input image (batch-size: 1)
  img = transform(im).unsqueeze(0)

  # propagate through the model
  outputs = model(img)

  # keep only predictions with 0.7+ confidence
  probas = outputs['pred_logits'].softmax(-1)[0, :, :-1]
  keep = probas.max(-1).values > 0.9

  # convert boxes from [0; 1] to image scales
  bboxes_scaled = rescale_bboxes(outputs['pred_boxes'][0, keep], im.size)
  return plot_results(im, probas[keep], bboxes_scaled)
  
  

inputs = gr.inputs.Image(type='pil', label="Original Image", shape=(600,600))
outputs = gr.outputs.Image(type="pil",label="Output Image")

examples = [
    ['horse.jpeg'],
    ['turtle.jpeg']
]

title = "DETR"
description = "Gradio demo for Facebook DETR. To use it, simply upload your image, or click one of the examples to load them. Read more at the links below."
article = "<p style='text-align: center'><a href='https://arxiv.org/abs/2005.12872'>End-to-End Object Detection with Transformers</a> | <a href='https://github.com/facebookresearch/detr'>Github Repo</a></p>"

gr.Interface(detr, inputs, outputs, title=title, description=description, article=article, examples=examples).launch()