from PIL import Image
import os
import argparse
import numpy as np
import json
import csv
import rasterio
import matplotlib
import folium
from pyproj import Proj, transform


%matplotlib inline


Image.MAX_IMAGE_PIXELS = 100000000000
# ingest the image
infile = "../data/mosaics/GrandJason_SWRightThird_Nov2019_transparent_mosaic_group1.tif"

img_dir = '..' + infile.split(".")[2]
prj_name = img_dir.split("/")[-1]
dataset = rasterio.open(infile)

# what is the name of this image
img_name = dataset.name
print('Image filename: {n}\n'.format(n=img_name))

# How many bands does this image have?
num_bands = dataset.count
print('Number of bands in image: {n}\n'.format(n=num_bands))

# How many rows and columns?
rows, cols = dataset.shape
print('Image size is: {r} rows x {c} columns\n'.format(r=rows, c=cols))

# Does the raster have a description or metadata?
desc = dataset.descriptions
metadata = dataset.meta

print('Raster description: {desc}\n'.format(desc=desc))

# What driver was used to open the raster?
driver = dataset.driver
print('Raster driver: {d}\n'.format(d=driver))

# What is the raster's projection?
proj = dataset.crs
print('Image projection:')
print(proj, '\n')

# What is the raster's "geo-transform"
gt = dataset.transform

print('Image geo-transform:\n{gt}\n'.format(gt=gt))

print('All raster metadata:')
print(metadata)
print('\n')

tile_height = tile_width = 1000
overlap = 80
stride = tile_height - overlap
start_num=0

#crop image into tiles
def crop(infile, tile_height, tile_width, stride, img_dict, prj_name):
    im = Image.open(infile) 
    img_width, img_height = im.size
    print(im.size)
    print(img_width * img_height / (tile_height - stride) / (tile_width - stride))
    count = 0
    for r in range(0, img_height-tile_height+1, stride):
        for c in range(0, img_width-tile_width+1, stride):
            #tile = im[r:r+100, c:c+100]
            box = (c, r, c+tile_width, r+tile_height)
            top_pixel = [c,r]
            img_dict[prj_name + "---" + str(count) + ".png"] = top_pixel
            count += 1
            yield im.crop(box)

#split image into heightxwidth patches
img = Image

img_dict = {}

# create the dir if it doesn't already exist
if not os.path.exists(img_dir):
    os.makedirs(img_dir)

# break it up into crops
for k, piece in enumerate(crop(infile, tile_height, tile_width, stride, img_dict, prj_name), start_num):
    img=Image.new('RGB', (tile_height, tile_width), (255, 255, 255))
    print(img.size)
    print(piece.size)
    img.paste(piece)
    image_name = prj_name + "---%s.png" % k
    path=os.path.join(img_dir, image_name)
    img.save(path)