import gradio as gr

import torch
from PIL import Image
import requests
from transformers import SamModel, SamProcessor
import numpy as np


def show_mask(mask, ax, random_color=False):
    if random_color:
        color = np.concatenate([np.random.random(3), np.array([0.6])], axis=0)
    else:
        color = np.array([30/255, 144/255, 255/255, 0.6])
    h, w = mask.shape[-2:]
    mask_image = mask.reshape(h, w, 1) * color.reshape(1, 1, -1)
    ax.imshow(mask_image)


def show_box(box, ax):
    x0, y0 = box[0], box[1]
    w, h = box[2] - box[0], box[3] - box[1]
    ax.add_patch(plt.Rectangle((x0, y0), w, h, edgecolor='green', facecolor=(0,0,0,0), lw=2))  

def show_boxes_on_image(raw_image, boxes):
    plt.figure(figsize=(10,10))
    plt.imshow(raw_image)
    for box in boxes:
      show_box(box, plt.gca())
    plt.axis('on')
    plt.show()


def show_points_on_image(raw_image, input_points, input_labels=None):
    plt.figure(figsize=(10,10))
    plt.imshow(raw_image)
    input_points = np.array(input_points)
    if input_labels is None:
      labels = np.ones_like(input_points[:, 0])
    else:
      labels = np.array(input_labels)
    show_points(input_points, labels, plt.gca())
    plt.axis('on')
    plt.show()

def show_points_and_boxes_on_image(raw_image, boxes, input_points, input_labels=None):
    plt.figure(figsize=(10,10))
    plt.imshow(raw_image)
    input_points = np.array(input_points)
    if input_labels is None:
      labels = np.ones_like(input_points[:, 0])
    else:
      labels = np.array(input_labels)
    show_points(input_points, labels, plt.gca())
    for box in boxes:
      show_box(box, plt.gca())
    plt.axis('on')
    plt.show()


def show_points_and_boxes_on_image(raw_image, boxes, input_points, input_labels=None):
    plt.figure(figsize=(10,10))
    plt.imshow(raw_image)
    input_points = np.array(input_points)
    if input_labels is None:
      labels = np.ones_like(input_points[:, 0])
    else:
      labels = np.array(input_labels)
    show_points(input_points, labels, plt.gca())
    for box in boxes:
      show_box(box, plt.gca())
    plt.axis('on')
    plt.show()


def show_points(coords, labels, ax, marker_size=375):
    pos_points = coords[labels==1]
    neg_points = coords[labels==0]
    ax.scatter(pos_points[:, 0], pos_points[:, 1], color='green', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)
    ax.scatter(neg_points[:, 0], neg_points[:, 1], color='red', marker='*', s=marker_size, edgecolor='white', linewidth=1.25)


def apply_masks_on_image(raw_image, masks, scores):
    if len(masks.shape) == 4:
      masks = masks.squeeze()
    if scores.shape[0] == 1:
      scores = scores.squeeze()

    nb_predictions = scores.shape[-1]
    fig, axes = plt.subplots(1, nb_predictions, figsize=(15, 15))

    for i, (mask, score) in enumerate(zip(masks, scores)):
      mask = mask.cpu().detach()
      axes[i].imshow(np.array(raw_image))
      show_mask(mask, axes[i])
      axes[i].title.set_text(f"Mask {i+1}, Score: {score.item():.3f}")
      axes[i].axis("off")
    plt.show()

def segment(imageUrl):
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
    processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")

    img_url = imageUrl#"https://huggingface.co/ybelkada/segment-anything/resolve/main/assets/car.png"
    raw_image = Image.open(requests.get(img_url, stream=True).raw).convert("RGB")
    input_points = [[[450, 600]]]  # 2D location of a window in the image

    inputs = processor(raw_image, input_points=input_points, return_tensors="pt").to(device)
    outputs = model(**inputs)

    masks = processor.image_processor.post_process_masks(
        outputs.pred_masks.cpu(), inputs["original_sizes"].cpu(), inputs["reshaped_input_sizes"].cpu()
    )
    scores = outputs.iou_scores
    return {"Masks": masks, "Scores": scores}

gr.Interface(fn=predict, 
             inputs=gr.Image(type="pil"),
             outputs=[{"type":"dataframe","name":"Categories Scores"},
             {"type":"dataframe","name":"Categories Labels"}],
  ).launch()