import cv2
import numpy as np
from typing import List
import pandas as pd


def detect_and_scale(image_path, model) -> List[tuple]:
    """
    Detect drawingtype (plantegning, fasade, snitt or situasjonskart).
    Filter by only plantegning, and scale bboxes to original image size.

    Args: 
        - image_path: path to uploaded image
        - model: Local stored object detection model
    Returns:
        List of tuples with bbox coordinates (x1,y1,x2,y2)
    
    """
    original_image = cv2.imread(image_path)
    orig_h, orig_w = original_image.shape[:2]

    results = model(image_path)
    detected_boxes = []
    
    for result in results:
        boxes = result.boxes
        conf_boxes = boxes.conf
        class_labels = result.names 

        yolo_h, yolo_w = result.orig_shape[:2]

        scale_x = orig_w / yolo_w
        scale_y = orig_h / yolo_h

        for box, cls in zip(boxes.xyxy, boxes.cls):
            label = class_labels[int(cls)]
            if label == "plantegning":
                x1, y1, x2, y2 = box.tolist()

                x1 = int(x1 * scale_x)
                y1 = int(y1 * scale_y)
                x2 = int(x2 * scale_x)
                y2 = int(y2 * scale_y)

                detected_boxes.append((x1, y1, x2, y2))
                #cv2.rectangle(original_image, (x1, y1), (x2, y2), (0, 255, 0), 2)
    
    return detected_boxes, original_image

def detect_and_plot_plantegning(model, image):
    results = model.predict(image, classes=1)
    annotated_img = results[0].plot()
    return annotated_img


def obj_to_pandas(objdet_results):
    detected_obj_list = []
    obj_count = 0
    for box in objdet_results:
        obj_count +=1
        detected_obj_list.append({"mask_id": obj_count, 'bboxes': box})
    
    return pd.DataFrame(detected_obj_list)


# ------------ AREAL MÅLEREN --------------------------

def detect_resize_walls(image_path, model):
    """ Detect and plot YOLO bounding boxes on the original image size """

    original_image = cv2.imread(image_path)
    orig_h, orig_w = original_image.shape[:2]

    results = model.predict(image_path)
    detected_boxes = []
    
    for result in results:
        boxes = result.boxes  
        class_labels = result.names

        yolo_h, yolo_w = result.orig_shape[:2]

        scale_x = orig_w / yolo_w
        scale_y = orig_h / yolo_h

        for box, cls in zip(boxes.xyxy, boxes.cls):
            label = class_labels[int(cls)]
            if label.lower() == "wall":
                x1, y1, x2, y2 = box.tolist()

                x1 = int(x1 * scale_x)
                y1 = int(y1 * scale_y)
                x2 = int(x2 * scale_x)
                y2 = int(y2 * scale_y)

                detected_boxes.append((x1, y1, x2, y2))
                cv2.rectangle(original_image, (x1, y1), (x2, y2), (0, 255, 0), 2)

    return detected_boxes

def remove_interior_walls(masks, bboxes):
    filtered_bboxes = []
    for bbox in bboxes:
        x1, y1, x2, y2 = bbox
        inside_mask = False  
        for mask in masks:
            mask_region = mask[y1:y2, x1:x2]
            mask_coverage = np.sum(mask_region > 0) / (mask_region.shape[0] * mask_region.shape[1])

            if mask_coverage > 0.8:  # If 80% or more of the bbox is covered by a mask, remove it
                inside_mask = True
                break

        if not inside_mask:
            filtered_bboxes.append(bbox)

    return filtered_bboxes

def reference_object(image_path, model, object_type="wall"):
    img = cv2.imread(image_path)
    results = model.predict(image_path, conf=0.5)
    longest_x = 0
    bbox = None
    longest_y = 0

    for result in results:
        boxes = result.boxes 
        class_labels = result.names 

        for box, cls in zip(boxes.xyxy, boxes.cls):
            label = class_labels[int(cls)] 

            if label.lower() == object_type:
                x1, y1, x2, y2 = map(int, box.tolist())
                length_x = x2 - x1
                length_y = y2 - y1

                if length_x > longest_x:
                    longest_x = length_x
                    bbox = (x1, y1, x2, y2)

                if length_y > longest_y:
                    longest_y = length_y

    if bbox:
        cv2.rectangle(img, (bbox[0], bbox[1]), (bbox[2], bbox[3]), (0, 255, 0), 2)

    return img, longest_x, longest_y