from ultralytics import FastSAM
from ultralytics.models.fastsam import FastSAMPrompt
import matplotlib.pyplot as plt

import os
import io
import numpy as np
import torch
import cv2
from PIL import Image

def fig2img(fig): 
    buf = io.BytesIO() 
    fig.savefig(buf) 
    buf.seek(0) 
    img = Image.open(buf) 
    return img 


def plot(
    annotations,
    prompt_process,
    bbox=None,
    points=None,
    point_label=None,
    mask_random_color=True,
    better_quality=True,
    retina=False,
    with_contours=True,
):
    """
    Plots annotations, bounding boxes, and points on images and saves the output.

    Args:
        annotations (list): Annotations to be plotted.
        output (str or Path): Output directory for saving the plots.
        bbox (list, optional): Bounding box coordinates [x1, y1, x2, y2]. Defaults to None.
        points (list, optional): Points to be plotted. Defaults to None.
        point_label (list, optional): Labels for the points. Defaults to None.
        mask_random_color (bool, optional): Whether to use random color for masks. Defaults to True.
        better_quality (bool, optional): Whether to apply morphological transformations for better mask quality.
            Defaults to True.
        retina (bool, optional): Whether to use retina mask. Defaults to False.
        with_contours (bool, optional): Whether to plot contours. Defaults to True.
    """

    # pbar = TQDM(annotations, total=len(annotations))
    for ann in annotations:
        result_name = os.path.basename(ann.path)
        image = ann.orig_img[..., ::-1]  # BGR to RGB
        original_h, original_w = ann.orig_shape
        # For macOS only
        # plt.switch_backend('TkAgg')
        fig = plt.figure(figsize=(original_w / 100, original_h / 100))
        # Add subplot with no margin.
        plt.subplots_adjust(top=1, bottom=0, right=1, left=0, hspace=0, wspace=0)
        plt.margins(0, 0)
        plt.gca().xaxis.set_major_locator(plt.NullLocator())
        plt.gca().yaxis.set_major_locator(plt.NullLocator())
        plt.imshow(image)

        if ann.masks is not None:
            masks = ann.masks.data
            if better_quality:
                if isinstance(masks[0], torch.Tensor):
                    masks = np.array(masks.cpu())
                for i, mask in enumerate(masks):
                    mask = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_CLOSE, np.ones((3, 3), np.uint8))
                    masks[i] = cv2.morphologyEx(mask.astype(np.uint8), cv2.MORPH_OPEN, np.ones((8, 8), np.uint8))

            prompt_process.fast_show_mask(
                masks,
                plt.gca(),
                random_color=mask_random_color,
                bbox=bbox,
                points=points,
                pointlabel=point_label,
                retinamask=retina,
                target_height=original_h,
                target_width=original_w,
            )

            if with_contours:
                contour_all = []
                temp = np.zeros((original_h, original_w, 1))
                for i, mask in enumerate(masks):
                    mask = mask.astype(np.uint8)
                    if not retina:
                        mask = cv2.resize(mask, (original_w, original_h), interpolation=cv2.INTER_NEAREST)
                    contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
                    contour_all.extend(iter(contours))
                cv2.drawContours(temp, contour_all, -1, (255, 255, 255), 2)
                color = np.array([0 / 255, 0 / 255, 1.0, 0.8])
                contour_mask = temp / 255 * color.reshape(1, 1, -1)
                plt.imshow(contour_mask)

        # Save the figure
        # save_path = Path(output) / result_name
        # save_path.parent.mkdir(exist_ok=True, parents=True)
        plt.axis("off")
        # plt.savefig(save_path, bbox_inches="tight", pad_inches=0, transparent=True)
        plt.close()
        # pbar.set_description(f"Saving {result_name} to {save_path}")

        return fig2img(fig)

# Create a FastSAM model
model = FastSAM("FastSAM-s.pt")  # or FastSAM-x.pt

def generateOutput(source):
    everything_results = model(source, retina_masks=True, imgsz=1024, conf=0.4, iou=0.9)
    # Prepare a Prompt Process object
    prompt_process = FastSAMPrompt(source, everything_results, device="cpu")
    # Everything prompt
    results = prompt_process.everything_prompt()
    
    outputimage = plot(annotations=results, prompt_process=prompt_process)

    return(outputimage)

title = "FastSAM Inference Trials"
description = "Shows the FastSAM related Inference Trials"
examples = [["Elephants.jpg"], ["Puppies.jpg"], ["photo2.JPG"], ["MultipleItems.jpg"]]
demo = gr.Interface(
    generateOutput, 
    inputs = [
        gr.Image(width=256, height=256, label="Input Image"), 
        ], 
    outputs = [
        gr.Image(width=256, height=256, label="Output"),
        ],
    title = title,
    description = description,
    examples = examples,
    cache_examples=False
)
demo.launch()