import torch
import cv2
import os
import torch.nn as nn
import numpy as np
import torchvision
from torchvision.ops import box_iou
from PIL import Image
import albumentations as A
from albumentations.pytorch import ToTensorV2
import cv2
import tqdm
import gc
from time import sleep
import shutil
from timeit import default_timer as timer
from typing import Tuple, Dict
import warnings
warnings.filterwarnings('ignore')

# apply nms algorithm
def apply_nms(orig_prediction, iou_thresh=0.3):
        # torchvision returns the indices of the bboxes to keep
        keep = torchvision.ops.nms(orig_prediction['boxes'], orig_prediction['scores'], iou_thresh)
        final_prediction = orig_prediction
        final_prediction['boxes'] = final_prediction['boxes'][keep]
        final_prediction['scores'] = final_prediction['scores'][keep]
        final_prediction['labels'] = final_prediction['labels'][keep]

        return final_prediction


def apply_nms2(orig_prediction, iou_thresh=0.3):
        # torchvision returns the indices of the bboxes to keep
        preds = []
        for prediction in orig_prediction:
          keep = torchvision.ops.nms(prediction['boxes'], prediction['scores'], iou_thresh)

          final_prediction = prediction
          final_prediction['boxes'] = final_prediction['boxes'][keep]
          final_prediction['scores'] = final_prediction['scores'][keep]
          final_prediction['labels'] = final_prediction['labels'][keep]
          preds.append(final_prediction)

        return preds

# Draw the bounding box
def plot_img_bbox(img, target):
        h,w,c = img.shape
        for box in (target['boxes']):
            xmin, ymin, xmax, ymax  = int((box[0].cpu()/1024)*w), int((box[1].cpu()/1024)*h), int((box[2].cpu()/1024)*w),int((box[3].cpu()/1024)*h)
            cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (0, 0, 255), 2)
            label = "palm"
            # Add the label and confidence score
            label = f'{label}'
            cv2.putText(img, label, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)

        # Display the image with detections
        #filename = 'pred.jpg'
        #cv2.imwrite(filename, img)
        return img

def crop(image,size=1024):
    #input = os.path.join(path,image)
    #img = cv2.imread(input)
    img = image.copy()
    H, W,_ = img.shape
    h = (H//size)
    w = (W//size)
    H1 = h*size
    W1 = w*size
    os.makedirs("images", exist_ok=True)
    images = []
    #images_truth = []
    locations = []

    if  H1 < H :
      chevauche_h = H-H1
      rest_h = 1024-chevauche_h
      val_h = H1-rest_h
      H2 = [x for x in range(0,H1,size)] +[val_h]
    else :
      H2 = [x for x in range(0,H1,size)]

    if W1 <W :
      chevauche_w = W-W1
      rest_w = 1024-chevauche_w
      val_w = W1-rest_w
      W2 = [x for x in range(0,W1,size)] +[val_w]
    else:
      W2 = [x for x in range(0,W1,size)]

    for i in H2:
        for j in W2:
          crop_img = img[i:i+size, j:j+size,:]
          name = "img_"+str(i)+"_"+str(j)+".png"
          ## csv file creation
          location = [i,i+size,j,j+size]
          locations.append(location)
          cv2.imwrite(os.path.join("images",name),crop_img)
          del crop_img
          gc.collect()
          #sleep(2)
    del H,H1,H2,W,W1,W2,h,w
    gc.collect()
    sleep(1)
    np.save("locations.npy",np.array(locations))

def inference(image,locations,model,test_transforms,device):
    n = 0
    os.makedirs("labels", exist_ok=True)
    for i,location in enumerate(locations):
        name = "img_"+str(location[0])+"_"+str(location[2])+".png"
        path = os.path.join("images",name)
        imgs = np.array(cv2.imread(path))
        transformed = test_transforms(image= imgs)
        image_transformed = transformed["image"]
        image_transformed = image_transformed.unsqueeze(0)
        image_transformed = image_transformed.to(device)

        model.eval()
        with torch.no_grad():
            predictions = model(image_transformed)

        del imgs,name,path,transformed,image_transformed
        gc.collect()
        sleep(1)

        nms_prediction = apply_nms2(predictions, iou_thresh=0.1)
        img = image[location[0]:location[1],location[2]:location[3],:]
        n = n+len(nms_prediction[0]['boxes'])

        for box in (nms_prediction[0]['boxes']):
            xmin, ymin, xmax, ymax = int(box[0].cpu()), int(box[1].cpu()), int(box[2].cpu()),int(box[3].cpu())
            cv2.rectangle(img, (xmin, ymin), (xmax, ymax), (255, 0, 0), 2)
            label = "palm"
            # Add the label and confidence score
            label = f'{label}'
            cv2.putText(img, label, (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (0, 0, 255), 2)
            del label
        #empty_image[location[0]:location[1],location[2]:location[3],:] = img
        label_name = "lab_"+str(location[0])+"_"+str(location[2])+".png"
        cv2.imwrite(os.path.join("labels",label_name),img)

        del label_name,img,nms_prediction,predictions
        gc.collect()
        sleep(1)

    return n

def create_new_ortho(locations,empty_image):
    for i,location in tqdm(enumerate(locations),total=len(locations)):
        name = "lab_"+str(location[0])+"_"+str(location[2])+".png"
        path = os.path.join("labels",name)
        img = np.array(cv2.imread(path))
        empty_image[location[0]:location[1],location[2]:location[3],:] = img
        if i%300==0:
            cv2.imwrite("img.png",empty_image)
            del img,name,path,empty_image
            gc.collect()
            #sleep(1)
            empty_image = np.array(cv2.imread("img.png"))

    cv2.imwrite("img.png",empty_image)
    empty_image = np.array(cv2.imread("img.png"))
    return empty_image