from typing import List
import face_recognition
from PIL import Image
from os import path as p
import os
import numpy as np

# Main method utilized to crop the group photos into various individual person photos to feed into the model
def getCroppedImages(image: Image.Image, cap = -1):
    """Takes a PIL image, and returns a list of PIL images with the cropped faces"""
    image = image.convert("RGB")
    face_locations = face_recognition.face_locations(np.array(image))
    
    face_locations_n = []
    outputs = []
    num = 0

    # Adjust dimensions of cropping to include more context
    width, height = image.size  # Get image dimensions
    for f in face_locations:
        if cap != -1 and num >= cap:
            break
        # Original coordinates: (top, right, bottom, left)
        top, right, bottom, left = f
        # Center of the face rectangle
        center_x = (left + right) / 2
        center_y = (top + bottom) / 2
        # Expanded dimensions
        box_width = (right - left) * 1.6
        box_height = (bottom - top) * 1.6
        new_left = int(center_x - box_width / 2)
        new_right = int(center_x + box_width / 2)
        new_top = int(center_y - box_height / 2)
        new_bottom = int(center_y + box_height / 2)
        # Clamp to image boundaries
        new_left = max(0, new_left)
        new_right = min(width, new_right)
        new_top = max(0, new_top)
        new_bottom = min(height, new_bottom)
        # Crop the image with the new coordinates
        c = (new_left, new_top, new_right, new_bottom)
        outputs.append(image.crop(c))
        face_locations_n.append(c)
        num += 1

    # Return face_locations_n, outputs
    return face_locations_n, outputs





# These following methods crop images but are not utilized in our app

# def getFaceLocationsAndFiles(input_path: str, output_dir: str, output_name: str,):
#     """
#     input_path: local or absolute path to the input image\n
#     output_dir: folder (local or absolute path) to save the output image to\n
#     output_name: name for the new image (do not include extension)\n
#     returns: list of face locations and list of paths to the cropped face images 
#     """
#     locs, imgs = getCroppedImages(Image.open(input_path))
#     output_paths = []
#     num = 0
#     for i in imgs:
#         path = p.join(output_dir, f"{output_name}_{num}.png")
#         i.save(path)
#         output_paths.append(p.abspath(path))
#         num += 1
        
#     return locs, output_paths


# def createCroppedSetFromImage(input_path: str, output_dir: str, output_name: str, cap = -1):
#     """
#     input_path: local or absolute path to the input image\n
#     output_dir: folder (local or absolute path) to save the output image to\n
#     output_name: name for the new image (do not include extension)\n
#     cap: set to -1 to process all faces detected, otherwise will limit faces to value\n
#     returns: list of absolute paths to the images
#     """
#     imgs = getCroppedImages(Image.open(input_path))[1]
    
#     num = 0
#     for i in imgs:
#         path = p.join(output_dir, f"{output_name}_{num}.png")
#         i.save(path)
#         num += 1
    

# def createCroppedSets(input_path: str, output_path: str):
#     """
#     Iterates through subdirectories in the input directory, making a duplicate of it in the output directory.
#     Then iterates through all images in the subdirectory, cropping each face and saving it to the output directory. 
    
#     For example for params "input" and "output", input/Dwayne Johnson/img.png will be cropped and
#     saved to output/Dwayne Johnson/0_0.png (repeated for all folder and the images they contain) 
    
#     WARNING: IF A DIRECTORY WITH THE SAME NAME AS THE OUTPUT DIRECTORY, IT WILL BE DELETED
#     """
#     # delete previous folder so there's no conflicts
#     if(p.exists(output_path)):
#         shutil.rmtree(output_path)
#     os.mkdir(output_path)
    
#     # iterate through all subdirectories in input directory
#     for dir in [name for name in os.listdir(input_path) if p.isdir(p.join(input_path, name))]:
#         sub_out = p.join(output_path, dir)
#         sub_in = p.join(input_path, dir)
#         os.mkdir(sub_out)
        
#         n = 0
#         # iterate through all files in subdirectory
#         for img in [name for name in os.listdir(sub_in) if p.isfile(p.join(sub_in, name))]:
#             createCroppedSetFromImage(p.join(sub_in, img), sub_out, n, 1)
#             n += 1