import cv2
import numpy as np
import pandas as pd
import statistics
from statistics import mode
from PIL import Image
import io
import google_sheet_Legend
import pypdfium2 as pdfium
import fitz  # PyMuPDF
import os
import random
import uuid
import math

def convert2img(data):
    pdf = pdfium.PdfDocument(data)
    page = pdf.get_page(0)
    pil_image = page.render().to_pil()
    pl1=np.array(pil_image)
    img = cv2.cvtColor(pl1, cv2.COLOR_RGB2BGR)
    img_cv2 = img 

    return img

def threshold(imgResult3):
  #gaus4 = cv2.GaussianBlur(imgResult3, (3,3),9)
  blur = cv2.blur(imgResult3,(7,7))
  gray4 = cv2.cvtColor(blur, cv2.COLOR_BGR2GRAY)
  outsu4 = cv2.threshold(gray4, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]
  return outsu4

def get_columns_info(outsu4, img):
  mask_clmns = np.ones(img.shape[:2], dtype="uint8") * 255
  mask_walls = np.ones(img.shape[:2], dtype="uint8") * 255
  contours, hierarchy = cv2.findContours(image=outsu4, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_NONE)
  p = [] #to save points of each contour
  for i, cnt in enumerate(contours):
    M = cv2.moments(cnt)
    if M['m00'] != 0.0:
        x1 = int(M['m10']/M['m00'])
        y1 = int(M['m01']/M['m00'])

    area = cv2.contourArea(cnt)
    if area > (881.0*2):
      perimeter = cv2.arcLength(cnt,True)
      #print(perimeter)
      cv2.drawContours(mask_walls, [cnt], -1, 0, -1)

    if area < (881.0 * 2) and area > 90:
      # maybe make it area < (881.0 * 1.5)
      p.append((x1,y1))
      #print(area)
      cv2.drawContours(mask_clmns, [cnt], -1, 0, -1)
  return p, mask_clmns, mask_walls

def get_text_from_pdf(input_pdf_path):
    #pdf_document = fitz.open(input_pdf_path)
    pdf_document = fitz.open("pdf", input_pdf_path)
    results = []

    for page_num in range(pdf_document.page_count):
        page = pdf_document[page_num]
        width, height = page.rect.width, page.rect.height  # Get page dimensions

        text_instances = [word for word in page.get_text("words") if word[4].startswith(("C", "c")) and len(word[4]) <= 5]

        page.apply_redactions()
    return text_instances

def calculate_midpoint(x1,y1,x2,y2):
  xm = int((x1 + x2) / 2)
  ym = int((y1 + y2) / 2)
  return (xm, ym)

### Can work with images as the rotation_matrix is applied
def getTextsPoints(x, page):
  point_list = []
  pt_clm = {}
  for h in x:
    pt = calculate_midpoint(h[0],h[1],h[2],h[3])
    pt = fitz.Point(pt[0], pt[1])
    pt = pt * page.rotation_matrix
    point_list.append(pt)
    pt_clm[pt] = h[4]
  return point_list, pt_clm

def distance(point1, point2):
    x1, y1 = point1
    x2, y2 = point2
    return np.sqrt((x1 - x2) ** 2 + (y1 - y2) ** 2)

def getNearestText(point_list, p):
  nearbyy = []
  selected_clm_point = [] #save the clmn for drawing cirlce on it
  dis = []
  txt_clmn = []
  for i in range(len(p)):
    nearest_point = min(point_list, key=lambda point: distance(point, p[i]))
    dist = distance(nearest_point, p[i])
    dis.append(dist)
    #if dist < 10:
    nearbyy.append(nearest_point)
    selected_clm_point.append(p[i])
    txt_clmn.append((nearest_point, p[i]))
  return nearbyy, selected_clm_point, txt_clmn

def color_groups(txtpts_ky_vlu):
  import random
  unique_labels = list(set(txtpts_ky_vlu.values()))
  def generate_rgb():
        return (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))  # RGB tuple
  key_colors = {key: generate_rgb() for key in unique_labels}  # Assign a unique RGB color to each key
  return key_colors

def getColumnsTypesKeyValue(nearbyy, pt_clm):
  words = []
  for i in range(len(nearbyy)):
    words.append(pt_clm[nearbyy[i]])
  return words

def generate_legend(found_tuple):
  word_freq = {}
  for word in found_tuple:
    if word in word_freq:
        word_freq[word] += 1
    else:
        word_freq[word] = 1
  data = word_freq
  df = pd.DataFrame(data.items(), columns=['Column Type', 'Count'])
  return df

def get_drawing_info(txt_clmn,txtpts_ky_vlu,key_colors):
  #Search for each word in the txt_clmn to get the word associated to it
  huge_list_clmn_clr_loc = []
  for text_location, column_location in txt_clmn:
    word = txtpts_ky_vlu[text_location]
    huge_list_clmn_clr_loc.append((text_location, column_location, word, key_colors[word]))
  return huge_list_clmn_clr_loc #text_location, column_location, word, color

def get_columns_info2(outsu4, img):
  mask_clmns = np.ones(img.shape[:2], dtype="uint8") * 255
  mask_walls = np.ones(img.shape[:2], dtype="uint8") * 255
  contours, hierarchy = cv2.findContours(image=outsu4, mode=cv2.RETR_EXTERNAL, method=cv2.CHAIN_APPROX_SIMPLE)
  p_column = [] #to save midpoints of each column
  p_wall = [] #to save midpoints of each wall
  wall_contour = []
  all_points = []
  wall_mid_and_full = {}

  for i, cnt in enumerate(contours):
    M = cv2.moments(cnt)
    if M['m00'] != 0.0:
        x1 = int(M['m10']/M['m00'])
        y1 = int(M['m01']/M['m00'])

    area = cv2.contourArea(cnt)
    if area > (881.0):
      perimeter = cv2.arcLength(cnt,True)
      p_wall.append((x1,y1))
      #print(perimeter)
      cv2.drawContours(mask_walls, [cnt], -1, 0, -1)
      wall_contour.append(cnt)
      all_points.append((x1,y1))
      wall_mid_and_full[(x1, y1)] = cnt

    if area < (881.0 * 2) and area > 90:
      # maybe make it area < (881.0 * 1.5)
      all_points.append((x1,y1))
      p_column.append((x1,y1))
      #print(area)
      cv2.drawContours(mask_clmns, [cnt], -1, 0, -1)
      wall_mid_and_full[(x1, y1)] = cnt

  return p_column, p_wall, mask_clmns, mask_walls, wall_contour, all_points, wall_mid_and_full

def get_all_wall_points(wall_contours):
    all_contours = []
    for cnt in wall_contours:
        contour_points = [(int(point[0][0]), int(point[0][1])) for point in cnt]
        all_contours.append(contour_points)
    return all_contours

def get_text_wall_text(input_pdf_path):
    #pdf_document = fitz.open(input_pdf_path)
    pdf_document = fitz.open("pdf", input_pdf_path)
    results = []

    for page_num in range(pdf_document.page_count):
        page = pdf_document[page_num]
        width, height = page.rect.width, page.rect.height

        text_instances = [word for word in page.get_text("words") if word[4].startswith(("w", "W")) and len(word[4]) <= 5]

        page.apply_redactions()
    return text_instances

def distance(p1, p2):
    return math.hypot(p1[0] - p2[0], p1[1] - p2[1])

def assign_walls_to_texts(text_locations, wall_locations, threshold=55):
    matched_texts = []
    matched_walls = []
    text_wall_pairs = []

    for text in text_locations:
        nearest_wall = min(wall_locations, key=lambda wall: distance(wall, text))
        dist = distance(nearest_wall, text)
        print(f"Text {text} -> Nearest wall {nearest_wall}, Distance: {dist:.2f}")

        if dist < threshold:
            matched_texts.append(text)
            matched_walls.append(nearest_wall)
            text_wall_pairs.append((text, nearest_wall))

    return matched_texts, matched_walls, text_wall_pairs

def mainfun(plan_path, segmented_img):
    #pdf_document = fitz.open(plan_path)
    print("Main started")
    pdf_document = fitz.open("pdf", plan_path)
    #print(f"type of plan_path: {type(plan_path)}")
    page = pdf_document[0]
    img_cv2 = convert2img(plan_path)
    rotation = page.rotation
    derotationMatrix=page.derotation_matrix
    nparr = np.frombuffer(segmented_img, np.uint8)
    segmented_img_cv2 = cv2.imdecode(nparr, cv2.IMREAD_UNCHANGED)
    outsu = threshold(segmented_img_cv2)
    column_points, mask_clmns, mask_walls = get_columns_info(outsu, img_cv2)
    texts_from_pdf = get_text_from_pdf(plan_path)
    text_points, txtpts_ky_vlu = getTextsPoints(texts_from_pdf, page)

    key_colors = color_groups(txtpts_ky_vlu)
    nearby, slct_clms, txt_clmn = getNearestText(text_points, column_points)
    columns_types_v = getColumnsTypesKeyValue(nearby, txtpts_ky_vlu)
    legend = generate_legend(columns_types_v)
    huge_list_clmn_clr_loc = get_drawing_info(txt_clmn, txtpts_ky_vlu, key_colors)
    column_midpoints, wall_midpoints, mask_clmns, mask_walls, wall_contours, all_points, midpoint_full_contour= get_columns_info2(outsu, img_cv2)
    wall_points = get_all_wall_points(wall_contours)
    wall_text = get_text_wall_text(plan_path)
    _,slct_walls, txt_wall = assign_walls_to_texts(text_points, all_points, 90)
    all_walls = []
    for wll in slct_walls:
        all_walls.append(midpoint_full_contour[wll])
    #This will be passed to Omar    
    selected_wall_contours = get_all_wall_points(all_walls)
    print("Main Khalaset")

    return {
        'legend': legend.to_dict(orient='records'),
        'num_columns_detected': len(column_points),
        'num_texts': len(text_points),
        'status': 'success'
        }