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import numpy as np
import argparse
import os, sys
from gui import GuiMain, GuiImage, GuiTag
import cv2
import logging
import magic
from tmsu import *
from util import *
from predictor import *
from PIL import Image

'''
Walk over all files for the given base directory and all subdirectories recursively.

Parameters:
args: Argument dict.
'''
def walk(tmsu, args):
    logger = logging.getLogger(__name__)
    logger.info("Walking files ...")

    mime = magic.Magic(mime=True)
    files = [os.path.abspath(os.path.join(dp, f)) for dp, dn, filenames in os.walk(args["file_dir"]) for f in filenames]
    logger.debug("Files: {}".format(files))
    logger.info("Number of files found: {}".format(len(files)))

    if args["index"] >= len(files):
        logger.error("Invalid start index. index = {}, number of files = {}".format(args["index"], len(files)))
        return

    if args["predict_images"]:
        backend = {
            "torch": Predictor.BackendTorch,
            "tensorflow": Predictor.BackendTensorflow,
            "keras": Predictor.BackendTensorflow
        }.get(args["predict_images_backend"])
        if backend == Predictor.BackendTorch:
            predictor = Predictor(Predictor.BackendTorch(top=args["predict_images_top"]))
        elif backend == Predictor.BackendTensorflow:
            predictor = Predictor(Predictor.BackendTensorflow(top=args["predict_images_top"], detail=(not args["predict_images_skip_detail"]), detail_factor=args["predict_images_detail_factor"]))

    for i in range(args["index"], len(files)):
        file_path = files[i]
        logger.info("Handling file {}, {}".format(i, file_path))
        tags = tmsu.tags(file_path)
        not_empty = bool(tags)
        logger.info("Existing tags: {}".format(tags))

        if (not_empty and args["skip_tagged"]):
            logger.info("Already tagged, skipping.")
            continue

        if args["open_system"]:
            open_system(file_path)

        # Detect MIME-type for file
        mime_type = mime.from_file(file_path)

        # Handle images
        if mime_type.split("/")[0] == "image":
            logger.debug("File is image")
            img = cv2.imread(file_path)
            img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
            if args["predict_images"]:
                logger.info("Predicting image tags ...")
                tags_predict = predictor.predict(img)
                logger.info("Predicted tags: {}".format(tags_predict))
                tags.update(tags_predict)
            if args["gui_tag"]:
                while(True): # For GUI inputs (rotate, ...)
                    logger.debug("Showing image GUI ...")
                    img_show = image_resize(img, width=args["gui_image_length"]) if img.shape[1] > img.shape[0] else image_resize(img, height=args["gui_image_length"])
                    #img_show = cv2.cvtColor(img_show, cv2.COLOR_BGR2RGB)
                    ret = GuiImage(i, file_path, img_show, tags).loop()
                    tags = set(ret[1]).difference({''})
                    if ret[0] == GuiImage.RETURN_ROTATE_90_CLOCKWISE:
                        img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)
                    elif ret[0] == GuiImage.RETURN_ROTATE_90_COUNTERCLOCKWISE:
                        img = cv2.rotate(img, cv2.ROTATE_90_COUNTERCLOCKWISE)
                    elif ret[0] == GuiImage.RETURN_NEXT:
                        break
                    elif ret[0] == GuiImage.RETURN_ABORT:
                        return
        else:
            if args["gui_tag"]:
                while(True):
                    logger.debug("Showing generic tagging GUI ...")
                    ret = GuiTag(i, file_path, tags).loop()
                    tags = set(ret[1]).difference({''})
                    if ret[0] == GuiTag.RETURN_NEXT:
                        break
                    elif ret[0] == GuiTag.RETURN_ABORT:
                        return

        if ((not args["gui_tag"]) and (not args["skip_prompt"])):
            tags = set(input_with_prefill("\nTags for file {}:\n".format(file_path), ','.join(tags)).split(","))

        logger.info("Tagging {}".format(tags))
        tmsu.tag(file_path, tags, untag=not_empty)


if __name__ == "__main__":
    parser = argparse.ArgumentParser(description='Tag multiple files using TMSU.')
    parser.add_argument('-b', '--base', nargs='?', default='.', type=dir_path, help='Base directory with database (default: %(default)s)')
    parser.add_argument('-f', '--file-dir', nargs='?', default='.', type=dir_path, help='File directory for walking (default: %(default)s)')
    parser.add_argument('-g', '--gui', nargs='?', const=1, default=False, type=bool, help='Show main GUI (default: %(default)s)')
    parser.add_argument('--tmsu-command', nargs='?', const=1, default="tmsu", type=str, help='TMSU command override (default: %(default)s)')
    parser.add_argument('--predict-images', nargs='?', const=1, default=False, type=bool, help='Use prediction for image tagging (default: %(default)s)')
    parser.add_argument('--predict-images-backend', nargs='?', const=1, choices=["torch", "tensorflow", "keras"], default="torch", type=str.lower, help='Determines which backend should be used for keyword prediction (default: %(default)s)')
    parser.add_argument('--predict-images-top', nargs='?', const=1, default=10, type=int, help='Defines how many top prediction keywords should be used (default: %(default)s)')
    parser.add_argument('--predict-images-detail-factor', nargs='?', const=1, default=2, type=int, help='Width factor for detail scan, multiplied by 224 for ResNet50 (default: %(default)s)')
    parser.add_argument('--predict-images-skip-detail', nargs='?', const=1, default=False, type=bool, help='Skip detail scan in image prediction (default: %(default)s)')
    parser.add_argument('--gui-tag', nargs='?', const=1, default=False, type=bool, help='Show GUI for tagging (default: %(default)s)')
    parser.add_argument('--gui-image-length', nargs='?', const=1, default=800, type=int, help='Length of longest side for preview (default: %(default)s)')
    parser.add_argument('--open-system', nargs='?', const=1, default=False, type=bool, help='Open all files with system default (default: %(default)s)')
    parser.add_argument('--skip-prompt', nargs='?', const=1, default=False, type=bool, help='Skip prompt for file tags (default: %(default)s)')
    parser.add_argument('--skip-tagged', nargs='?', const=1, default=False, type=bool, help='Skip already tagged files (default: %(default)s)')
    parser.add_argument('-i', '--index', nargs='?', const=1, default=0, type=int, help='Start tagging at the given file index (default: %(default)s)')
    parser.add_argument('-v', '--verbose', action="count", default=0, help="Verbosity level")
    args = parser.parse_args()

    if args.verbose == 0:
        log_level = logging.WARNING
    elif args.verbose == 1:
        log_level = logging.INFO
    elif args.verbose >= 2:
        log_level = logging.DEBUG

    logging.basicConfig(stream=sys.stdout, level=log_level)
    logger = logging.getLogger(__name__)

    args = {
        "base": args.base,
        "file_dir": args.file_dir,
        "gui": args.gui,
        "tmsu_command": args.tmsu_command,
        "predict_images": args.predict_images,
        "predict_images_backend": args.predict_images_backend,
        "predict_images_top": args.predict_images_top,
        "predict_images_detail_factor": args.predict_images_detail_factor,
        "predict_images_skip_detail": args.predict_images_skip_detail,
        "gui_tag": args.gui_tag,
        "gui_image_length": args.gui_image_length,
        "open_system": args.open_system,
        "skip_prompt": args.skip_prompt,
        "skip_tagged": args.skip_tagged,
        "index": args.index,
        "verbosity": args.verbose
    }

    logger.debug("args = {}".format(args))

    if args["gui"]:
        logger.debug("Starting main GUI ...")
        args = GuiMain(args).loop()

    tmsu = TMSU(args["base"], args["tmsu_command"])

    if tmsu.status:
        walk(tmsu, args)