import cv2
import numpy as np
import math
import torch
import random
from PIL import Image

from torch.utils.data import DataLoader
from torchvision.transforms import Resize

torch.manual_seed(12345)
random.seed(12345)
np.random.seed(12345)

device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")

class WireframeExtractor:

    def __call__(self, image: np.ndarray):
        """
        Extract corners of wireframe from a barnacle image
        :param image: Numpy RGB image of shape (W, H, 3)
        :return [x1, y1, x2, y2]
        """
        h, w = image.shape[:2]
        imghsv = cv2.cvtColor(image, cv2.COLOR_RGB2HSV)
        hsvblur = cv2.GaussianBlur(imghsv, (9, 9), 0)

        lower = np.array([70, 20, 20])
        upper = np.array([130, 255, 255])

        color_mask = cv2.inRange(hsvblur, lower, upper)

        invert = cv2.bitwise_not(color_mask)

        contours, _ = cv2.findContours(invert, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)

        max_contour = contours[0]
        largest_area = 0
        for index, contour in enumerate(contours):
            area = cv2.contourArea(contour)
            if area > largest_area:
                if cv2.pointPolygonTest(contour, (w / 2, h / 2), False) == 1:
                    largest_area = area
                    max_contour = contour

        x, y, w, h = cv2.boundingRect(max_contour)
        # return [x, y, x + w, y + h]
        return x,y,w,h

wireframe_extractor = WireframeExtractor()

def show_anns(anns):
    if len(anns) == 0:
        return
    sorted_anns = sorted(anns, key=(lambda x: x['area']), reverse=True)
    ax = plt.gca()
    ax.set_autoscale_on(False)
    polygons = []
    color = []
    for ann in sorted_anns:
        m = ann['segmentation']
        img = np.ones((m.shape[0], m.shape[1], 3))
        color_mask = np.random.random((1, 3)).tolist()[0]
        for i in range(3):
            img[:,:,i] = color_mask[i]
        ax.imshow(np.dstack((img, m*0.35)))
    
from segment_anything import sam_model_registry, SamAutomaticMaskGenerator, SamPredictor

model = sam_model_registry["default"](checkpoint="./sam_vit_h_4b8939.pth")
model.to(device)

mask_generator = SamAutomaticMaskGenerator(model)

import gradio as gr

import matplotlib.pyplot as plt
import io

def check_circularity(segmentation):
  img_u8 = segmentation.astype(np.uint8)
  im_gauss = cv2.GaussianBlur(img_u8, (5, 5), 0)
  ret, thresh = cv2.threshold(im_gauss, 0, 255, cv2.THRESH_BINARY)
  contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

  con = contours[0]
  perimeter = cv2.arcLength(con, True)
  area = cv2.contourArea(con)
  if perimeter != 0:
    circularity = 4*math.pi*(area/(perimeter*perimeter))
    if 0.8 < circularity < 1.2:
        return True
    else:
      return circularity

def count_barnacles(image_raw, split_num, progress=gr.Progress()):
    progress(0, desc="Finding bounding wire")

    corners = wireframe_extractor(image_raw)
    print(corners) # (0, 0, 1254, 1152)

    cropped_image = image_raw[corners[1]:corners[3]+corners[1], corners[0]:corners[2]+corners[0], :]

    print(cropped_image.shape)

    split_num = 2

    x_inc = int(cropped_image.shape[0]/split_num)
    y_inc = int(cropped_image.shape[1]/split_num)
    startx = -x_inc

    mask_counter = 0
    good_masks = []
    centers = []

    for r in range(0, split_num):
        startx += x_inc
        starty = -y_inc
        for c in range(0, split_num):
            starty += y_inc

            small_image = cropped_image[starty:starty+y_inc, startx:startx+x_inc, :]

            # plt.figure()
            # plt.imshow(small_image)
            # plt.axis('on')
            progress(0, desc=f"Generating masks for crop {r*split_num + c}/{split_num ** 2}")
            masks = mask_generator.generate(small_image)
            num_masks = len(masks)
            
            for idx, mask in enumerate(masks):
                progress(float(idx)/float(num_masks), desc=f"Processing masks for crop {r*split_num + c}/{split_num ** 2}")
                circular = check_circularity(mask['segmentation'])
                if circular and mask['area']>500 and mask['area'] < 10000:
                    mask_counter += 1
                    good_masks.append(mask)
                    box = mask['bbox']
                    centers.append((box[0] + box[2]/2 + corners[0] + startx, box[1] + box[3]/2 + corners[1] + starty))

    
    progress(0, desc="Generating Plot")
    # Create a figure with a size of 10 inches by 10 inches
    fig = plt.figure(figsize=(10, 10))

    # Display the image using the imshow() function
    # plt.imshow(cropped_image)
    plt.imshow(image_raw)

    # Call the custom function show_anns() to plot annotations on top of the image
    # show_anns(good_masks)

    for coord in centers: 
        plt.scatter(coord[0], coord[1], marker="x", color="red", s=32)

    # Turn off the axis
    plt.axis('off')

    # Get the plot as a numpy array
    # buf = io.BytesIO()
    # plt.savefig(buf, format='png', bbox_inches='tight', pad_inches=0)
    # buf.seek(0)
    # img_arr = np.frombuffer(buf.getvalue(), dtype=np.uint8)
    # buf.close()

    # # Decode the numpy array to an image
    # annotated = cv2.imdecode(img_arr, 1)
    # annotated = cv2.cvtColor(annotated, cv2.COLOR_BGR2RGB)

    # # Close the figure
    # plt.close(fig)
    
    
    # return annotated, mask_counter, centers

    good_centers = []
    for point in centers:
        is_good = True
        for prev_point in good_centers:
            if (point[0] - prev_point[0]) ** 2 + (point[1] + prev_point[1]) ** 2 < 200:
                is_good = False
        if is_good:
            good_centers.append(point)
    
    return fig, len(good_centers), good_centers

demo = gr.Interface(count_barnacles,
                    inputs=[
                        gr.Image(type="numpy", label="Input Image"),
                    ],
                    outputs=[
                        # gr.Image(type="numpy", label="Annotated Image"),
                        gr.Plot(label="Annotated Image"),
                        gr.Number(label="Predicted Number of Barnacles"),
                        gr.Dataframe(type="array", headers=["x", "y"], label="Mask centers")
                        # gr.Number(label="Actual Number of Barnacles"),
                        # gr.Number(label="Custom Metric")
                    ])
                    # examples="examples")
demo.queue(concurrency_count=1).launch()