import os 
import sys
os.system('pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu102/torch1.9/index.html')
os.system("pip install git+https://github.com/openai/CLIP.git")
import gradio as gr

# clone and install Detic
os.system("git clone https://github.com/facebookresearch/Detic.git --recurse-submodules")
sys.path.append('./Detic')

# Install detectron2
import torch

# Some basic setup:
# Setup detectron2 logger
import detectron2
from detectron2.utils.logger import setup_logger
setup_logger()

# import some common libraries
import sys
import numpy as np
import os, json, cv2, random

# import some common detectron2 utilities
from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog, DatasetCatalog

# Detic libraries
sys.path.insert(0, 'third_party/CenterNet2/projects/CenterNet2/')
from centernet.config import add_centernet_config
from detic.config import add_detic_config
from detic.modeling.utils import reset_cls_test

# Build the detector and download our pretrained weights
cfg = get_cfg()
add_centernet_config(cfg)
add_detic_config(cfg)
cfg.MODEL.DEVICE='cpu'
cfg.merge_from_file("configs/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.yaml")
cfg.MODEL.WEIGHTS = 'https://dl.fbaipublicfiles.com/detic/Detic_LCOCOI21k_CLIP_SwinB_896b32_4x_ft4x_max-size.pth'
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5  # set threshold for this model
cfg.MODEL.ROI_BOX_HEAD.ZEROSHOT_WEIGHT_PATH = 'rand'
cfg.MODEL.ROI_HEADS.ONE_CLASS_PER_PROPOSAL = True # For better visualization purpose. Set to False for all classes.
predictor = DefaultPredictor(cfg)

# Setup the model's vocabulary using build-in datasets

BUILDIN_CLASSIFIER = {
    'lvis': 'datasets/metadata/lvis_v1_clip_a+cname.npy',
    'objects365': 'datasets/metadata/o365_clip_a+cnamefix.npy',
    'openimages': 'datasets/metadata/oid_clip_a+cname.npy',
    'coco': 'datasets/metadata/coco_clip_a+cname.npy',
}

BUILDIN_METADATA_PATH = {
    'lvis': 'lvis_v1_val',
    'objects365': 'objects365_v2_val',
    'openimages': 'oid_val_expanded',
    'coco': 'coco_2017_val',
}

vocabulary = 'lvis' # change to 'lvis', 'objects365', 'openimages', or 'coco'
metadata = MetadataCatalog.get(BUILDIN_METADATA_PATH[vocabulary])
classifier = BUILDIN_CLASSIFIER[vocabulary]
num_classes = len(metadata.thing_classes)
reset_cls_test(predictor.model, classifier, num_classes)

os.system("wget https://web.eecs.umich.edu/~fouhey/fun/desk/desk.jpg")

def inference(img):

    im = cv2.imread(img)

    outputs = predictor(im)
    v = Visualizer(im[:, :, ::-1], metadata)
    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))
    cv2_imshow(out.get_image()[:, :, ::-1])

    return Image.fromarray(np.uint8(out.get_image())).convert('RGB')
    
title = "Detectron 2"
description = "Gradio demo for Detectron 2: A PyTorch-based modular object detection library. 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://ai.facebook.com/blog/-detectron2-a-pytorch-based-modular-object-detection-library-/' target='_blank'>Detectron2: A PyTorch-based modular object detection library</a> | <a href='https://github.com/facebookresearch/detectron2' target='_blank'>Github Repo</a></p>"

examples = [['desk.jpg']]
gr.Interface(inference, inputs=gr.inputs.Image(type="filepath"), outputs=gr.outputs.Image(type="pil"),enable_queue=True, title=title,
    description=description,
    article=article,
    examples=examples).launch()