"""
DEIMv2: Real-Time Object Detection Meets DINOv3
Copyright (c) 2025 The DEIMv2 Authors. All Rights Reserved.
---------------------------------------------------------------------------------
Modified from D-FINE (https://github.com/Peterande/D-FINE)
Copyright (c) 2024 The D-FINE Authors. All Rights Reserved.
"""


import cv2
import numpy as np
import axengine as ort
import torch
import torchvision
import torchvision.transforms as T
from PIL import Image, ImageDraw
from torch import nn
import torch.nn.functional as F


def mod(a, b):
    out = a - a // b * b
    return out


class PostProcessor(nn.Module):
    __share__ = [
        'num_classes',
        'use_focal_loss',
        'num_top_queries',
        'remap_mscoco_category'
    ]

    def __init__(
        self,
        num_classes=80,
        use_focal_loss=True,
        num_top_queries=300,
        remap_mscoco_category=False
    ) -> None:
        super().__init__()
        self.use_focal_loss = use_focal_loss
        self.num_top_queries = num_top_queries
        self.num_classes = int(num_classes)
        self.remap_mscoco_category = remap_mscoco_category
        self.deploy_mode = False

    def extra_repr(self) -> str:
        return f'use_focal_loss={self.use_focal_loss}, num_classes={self.num_classes}, num_top_queries={self.num_top_queries}'

    # def forward(self, outputs, orig_target_sizes):
    def forward(self, outputs, orig_target_sizes: torch.Tensor):
        logits, boxes = outputs['pred_logits'], outputs['pred_boxes']
        # orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)

        bbox_pred = torchvision.ops.box_convert(boxes, in_fmt='cxcywh', out_fmt='xyxy')
        bbox_pred *= orig_target_sizes.repeat(1, 2).unsqueeze(1)

        if self.use_focal_loss:
            scores = F.sigmoid(logits)
            scores, index = torch.topk(scores.flatten(1), self.num_top_queries, dim=-1)
            # labels = index % self.num_classes
            labels = mod(index, self.num_classes)
            index = index // self.num_classes
            boxes = bbox_pred.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, bbox_pred.shape[-1]))

        else:
            scores = F.softmax(logits)[:, :, :-1]
            scores, labels = scores.max(dim=-1)
            if scores.shape[1] > self.num_top_queries:
                scores, index = torch.topk(scores, self.num_top_queries, dim=-1)
                labels = torch.gather(labels, dim=1, index=index)
                boxes = torch.gather(boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1]))

        if self.deploy_mode:
            return labels, boxes, scores

        if self.remap_mscoco_category:
            from ..data.dataset import mscoco_label2category
            labels = torch.tensor([mscoco_label2category[int(x.item())] for x in labels.flatten()])\
                .to(boxes.device).reshape(labels.shape)

        results = []
        for lab, box, sco in zip(labels, boxes, scores):
            result = dict(labels=lab, boxes=box, scores=sco)
            results.append(result)

        return results


    def deploy(self, ):
        self.eval()
        self.deploy_mode = True
        return self


def resize_with_aspect_ratio(image, size, interpolation=Image.BILINEAR):
    """Resizes an image while maintaining aspect ratio and pads it."""
    original_width, original_height = image.size
    ratio = min(size / original_width, size / original_height)
    new_width = int(original_width * ratio)
    new_height = int(original_height * ratio)
    image = image.resize((new_width, new_height), interpolation)

    # Create a new image with the desired size and paste the resized image onto it
    new_image = Image.new("RGB", (size, size))
    new_image.paste(image, ((size - new_width) // 2, (size - new_height) // 2))
    return new_image, ratio, (size - new_width) // 2, (size - new_height) // 2


def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.4):
    result_images = []
    for i, im in enumerate(images):
        draw = ImageDraw.Draw(im)
        scr = scores[i]
        lab = labels[i][scr > thrh]
        box = boxes[i][scr > thrh]
        scr = scr[scr > thrh]

        ratio = ratios[i]
        pad_w, pad_h = paddings[i]

        for lbl, bb in zip(lab, box):
            # Adjust bounding boxes according to the resizing and padding
            bb = [
                (bb[0] - pad_w) / ratio,
                (bb[1] - pad_h) / ratio,
                (bb[2] - pad_w) / ratio,
                (bb[3] - pad_h) / ratio,
            ]
            draw.rectangle(bb, outline='red')
            draw.text((bb[0], bb[1]), text=str(lbl), fill='blue')

        result_images.append(im)
    return result_images


def process_image(sess, im_pil, size=640, model_size='s'):
    post_processor = PostProcessor().deploy()
    # Resize image while preserving aspect ratio
    resized_im_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(im_pil, size)
    orig_size = torch.tensor([[resized_im_pil.size[1], resized_im_pil.size[0]]])

    transforms = T.Compose([
            T.ToTensor(),
            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 
                if model_size not in ['atto', 'femto', 'pico', 'n'] 
                else T.Lambda(lambda x: x)
        ])
    im_data = transforms(resized_im_pil).unsqueeze(0)

    output = sess.run(
        output_names=None,
        input_feed={'images': im_data.numpy()}
    )
    
    output = {"pred_logits": torch.from_numpy(output[0]), "pred_boxes": torch.from_numpy(output[1])}
    output = post_processor(output, orig_size)
    labels, boxes, scores = output[0].numpy(), output[1].numpy(), output[2].numpy()

    result_images = draw(
        [im_pil], labels, boxes, scores,
        [ratio], [(pad_w, pad_h)]
    )
    result_images[0].save('result.jpg')
    print("Image processing complete. Result saved as 'result.jpg'.")


def process_video(sess, video_path, size=640, model_size='s'):
    cap = cv2.VideoCapture(video_path)

    # Get video properties
    fps = cap.get(cv2.CAP_PROP_FPS)
    orig_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    orig_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))

    # Define the codec and create VideoWriter object
    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
    out = cv2.VideoWriter('onnx_result.mp4', fourcc, fps, (orig_w, orig_h))

    frame_count = 0
    print("Processing video frames...")
    while cap.isOpened():
        ret, frame = cap.read()
        if not ret:
            break

        # Convert frame to PIL image
        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))

        # Resize frame while preserving aspect ratio
        resized_frame_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(frame_pil, size)
        orig_size = torch.tensor([[resized_frame_pil.size[1], resized_frame_pil.size[0]]])

        transforms = T.Compose([
                T.ToTensor(),
                T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 
                    if model_size not in ['atto', 'femto', 'pico', 'n'] 
                    else T.Lambda(lambda x: x)
            ])
        im_data = transforms(resized_frame_pil).unsqueeze(0)

        output = sess.run(
            output_names=None,
            input_feed={'images': im_data.numpy(), "orig_target_sizes": orig_size.numpy()}
        )

        labels, boxes, scores = output

        # Draw detections on the original frame
        result_images = draw(
            [frame_pil], labels, boxes, scores,
            [ratio], [(pad_w, pad_h)]
        )
        frame_with_detections = result_images[0]

        # Convert back to OpenCV image
        frame = cv2.cvtColor(np.array(frame_with_detections), cv2.COLOR_RGB2BGR)

        # Write the frame
        out.write(frame)
        frame_count += 1

        if frame_count % 10 == 0:
            print(f"Processed {frame_count} frames...")

    cap.release()
    out.release()
    print("Video processing complete. Result saved as 'result.mp4'.")


def main(args):
    """Main function."""
    # Load the ONNX model
    sess = ort.InferenceSession(args.axmodel)
    size = sess.get_inputs()[0].shape[2]

    input_path = args.input

    try:
        # Try to open the input as an image
        im_pil = Image.open(input_path).convert('RGB')
        process_image(sess, im_pil, size, args.model_size)
    except IOError:
        # Not an image, process as video
        process_video(sess, input_path, size, args.model_size)


if __name__ == '__main__':
    import argparse
    parser = argparse.ArgumentParser()
    parser.add_argument('--axmodel', type=str, default="compiled.axmodel", help='Path to the axmodel model file.')
    parser.add_argument('--input', type=str, required=True, help='Path to the input image or video file.')
    parser.add_argument('-ms', '--model-size', type=str, required=True, choices=['atto', 'femto', 'pico', 'n', 's', 'm', 'l', 'x'], 
                        help='Model size')
    args = parser.parse_args()
    main(args)