# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import cv2
from shapely.wkt import loads as wkt_loads
import tifffile as tiff


# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

# Any results you write to the current directory are saved as output.

# The code is for python 2.7. Parts of it are taken from other posts/kernels.
# Good luck!


def _get_image_names(base_path, imageId):
    '''
    Get the names of the tiff files
    '''
    d = {'3': path.join(base_path,'three_band/{}.tif'.format(imageId)),             # (3, 3348, 3403)
         'A': path.join(base_path,'sixteen_band/{}_A.tif'.format(imageId)),         # (8, 134, 137)
         'M': path.join(base_path,'sixteen_band/{}_M.tif'.format(imageId)),         # (8, 837, 851)
         'P': path.join(base_path,'sixteen_band/{}_P.tif'.format(imageId)),         # (3348, 3403)
         }
    return d
    
def get_images(imageId, img_key = None):
    '''
    Load images correspoding to imageId

    Parameters
    ----------
    imageId : str
        imageId as used in grid_size.csv
    img_key : {None, '3', 'A', 'M', 'P'}, optional
        Specify this to load single image
        None loads all images and returns in a dict
        '3' loads image from three_band/
        'A' loads '_A' image from sixteen_band/
        'M' loads '_M' image from sixteen_band/
        'P' loads '_P' image from sixteen_band/

    Returns
    -------
    images : dict
        A dict of image data from TIFF files as numpy array
    '''
    img_names = get_image_names(imageId)
    images = dict()
    if img_key is None:
        for k in img_names.keys():
            images[k] = tiff.imread(img_names[k])
    else:
        images[img_key] = tiff.imread(img_names[img_key])
    return images
    
def get_image_names(imageId):
    '''
    Get the names of the tiff files
    '''
    d = {'3': '{}/three_band/{}.tif'.format(inDir, imageId),
         'A': '{}/sixteen_band/{}_A.tif'.format(inDir, imageId),
         'M': '{}/sixteen_band/{}_M.tif'.format(inDir, imageId),
         'P': '{}/sixteen_band/{}_P.tif'.format(inDir, imageId),
         }
    return d

    
def get_size(imageId):
    """
    Get the grid size of the image

    Parameters
    ----------
    imageId : str
        imageId as used in grid_size.csv
    """
    xmax, ymin = gs[gs.ImageId == imageId].iloc[0,1:].astype(float)
    W, H = get_images(imageId, '3')['3'].shape[1:]
    return (xmax, ymin, W, H)



def _convert_coordinates_to_raster(coords, img_size, xymax):
    Xmax,Ymax = xymax
    H,W = img_size
    W1 = 1.0*W*W/(W+1)
    H1 = 1.0*H*H/(H+1)
    xf = W1/Xmax
    yf = H1/Ymax
    coords[:,1] *= yf
    coords[:,0] *= xf
    coords_int = np.round(coords).astype(np.int32)
    return coords_int


def _get_xmax_ymin(grid_sizes_panda, imageId):
    xmax, ymin = grid_sizes_panda[grid_sizes_panda.ImageId == imageId].iloc[0,1:].astype(float)
    return (xmax,ymin)


def _get_polygon_list(wkt_list_pandas, imageId, cType):
    df_image = wkt_list_pandas[wkt_list_pandas.ImageId == imageId]
    multipoly_def = df_image[df_image.ClassType == cType].MultipolygonWKT
    polygonList = None
    if len(multipoly_def) > 0:
        assert len(multipoly_def) == 1
        polygonList = wkt_loads(multipoly_def.values[0])
    return polygonList


def _get_and_convert_contours(polygonList, raster_img_size, xymax):
    perim_list = []
    interior_list = []
    if polygonList is None:
        return None
    for k in range(len(polygonList)):
        poly = polygonList[k]
        perim = np.array(list(poly.exterior.coords))
        perim_c = _convert_coordinates_to_raster(perim, raster_img_size, xymax)
        perim_list.append(perim_c)
        for pi in poly.interiors:
            interior = np.array(list(pi.coords))
            interior_c = _convert_coordinates_to_raster(interior, raster_img_size, xymax)
            interior_list.append(interior_c)
    return perim_list,interior_list


def _plot_mask_from_contours(raster_img_size, contours, class_value):
    #img_mask = np.zeros(raster_img_size,np.uint8)
    if contours is None:
        return img_mask
    perim_list,interior_list = contours
    cv2.fillPoly(img_mask,perim_list,class_value)
    #cv2.fillPoly(img_mask,interior_list,0)
    return img_mask


def generate_mask_for_image_and_class(raster_size, imageId, class_type, grid_sizes_panda,
                                     wkt_list_pandas):
    xymax = _get_xmax_ymin(grid_sizes_panda,imageId)
    polygon_list = _get_polygon_list(wkt_list_pandas,imageId,class_type)
    contours = _get_and_convert_contours(polygon_list,raster_size,xymax)
    mask = _plot_mask_from_contours(raster_size,contours,class_type)
    return mask


inDir = '../input'


# read the training data from train_wkt_v4.csv
df = pd.read_csv(inDir + '/train_wkt_v4.csv')

# grid size will also be needed later..
gs = pd.read_csv(inDir + '/grid_sizes.csv', names=['ImageId', 'Xmax', 'Ymin'], skiprows=1)

xmax, ymin, W, H=get_size("6120_2_2")
img_mask = np.zeros((W,H),np.uint8)
for i in range(1,10):
    mask = generate_mask_for_image_and_class((W,H),"6120_2_2",i,gs,df)
cv2.imwrite("mask.png",mask)
#np.savetxt('maximums.txt', mask.astype(int),delimt)# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import cv2
from shapely.wkt import loads as wkt_loads
import tifffile as tiff


# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

# Any results you write to the current directory are saved as output.

# The code is for python 2.7. Parts of it are taken from other posts/kernels.
# Good luck!


def _get_image_names(base_path, imageId):
    '''
    Get the names of the tiff files
    '''
    d = {'3': path.join(base_path,'three_band/{}.tif'.format(imageId)),             # (3, 3348, 3403)
         'A': path.join(base_path,'sixteen_band/{}_A.tif'.format(imageId)),         # (8, 134, 137)
         'M': path.join(base_path,'sixteen_band/{}_M.tif'.format(imageId)),         # (8, 837, 851)
         'P': path.join(base_path,'sixteen_band/{}_P.tif'.format(imageId)),         # (3348, 3403)
         }
    return d
    
def get_images(imageId, img_key = None):
    '''
    Load images correspoding to imageId

    Parameters
    ----------
    imageId : str
        imageId as used in grid_size.csv
    img_key : {None, '3', 'A', 'M', 'P'}, optional
        Specify this to load single image
        None loads all images and returns in a dict
        '3' loads image from three_band/
        'A' loads '_A' image from sixteen_band/
        'M' loads '_M' image from sixteen_band/
        'P' loads '_P' image from sixteen_band/

    Returns
    -------
    images : dict
        A dict of image data from TIFF files as numpy array
    '''
    img_names = get_image_names(imageId)
    images = dict()
    if img_key is None:
        for k in img_names.keys():
            images[k] = tiff.imread(img_names[k])
    else:
        images[img_key] = tiff.imread(img_names[img_key])
    return images
    
def get_image_names(imageId):
    '''
    Get the names of the tiff files
    '''
    d = {'3': '{}/three_band/{}.tif'.format(inDir, imageId),
         'A': '{}/sixteen_band/{}_A.tif'.format(inDir, imageId),
         'M': '{}/sixteen_band/{}_M.tif'.format(inDir, imageId),
         'P': '{}/sixteen_band/{}_P.tif'.format(inDir, imageId),
         }
    return d

    
def get_size(imageId):
    """
    Get the grid size of the image

    Parameters
    ----------
    imageId : str
        imageId as used in grid_size.csv
    """
    xmax, ymin = gs[gs.ImageId == imageId].iloc[0,1:].astype(float)
    W, H = get_images(imageId, '3')['3'].shape[1:]
    return (xmax, ymin, W, H)



def _convert_coordinates_to_raster(coords, img_size, xymax):
    Xmax,Ymax = xymax
    H,W = img_size
    W1 = 1.0*W*W/(W+1)
    H1 = 1.0*H*H/(H+1)
    xf = W1/Xmax
    yf = H1/Ymax
    coords[:,1] *= yf
    coords[:,0] *= xf
    coords_int = np.round(coords).astype(np.int32)
    return coords_int


def _get_xmax_ymin(grid_sizes_panda, imageId):
    xmax, ymin = grid_sizes_panda[grid_sizes_panda.ImageId == imageId].iloc[0,1:].astype(float)
    return (xmax,ymin)


def _get_polygon_list(wkt_list_pandas, imageId, cType):
    df_image = wkt_list_pandas[wkt_list_pandas.ImageId == imageId]
    multipoly_def = df_image[df_image.ClassType == cType].MultipolygonWKT
    polygonList = None
    if len(multipoly_def) > 0:
        assert len(multipoly_def) == 1
        polygonList = wkt_loads(multipoly_def.values[0])
    return polygonList


def _get_and_convert_contours(polygonList, raster_img_size, xymax):
    perim_list = []
    interior_list = []
    if polygonList is None:
        return None
    for k in range(len(polygonList)):
        poly = polygonList[k]
        perim = np.array(list(poly.exterior.coords))
        perim_c = _convert_coordinates_to_raster(perim, raster_img_size, xymax)
        perim_list.append(perim_c)
        for pi in poly.interiors:
            interior = np.array(list(pi.coords))
            interior_c = _convert_coordinates_to_raster(interior, raster_img_size, xymax)
            interior_list.append(interior_c)
    return perim_list,interior_list


def _plot_mask_from_contours(raster_img_size, contours, class_value):
    #img_mask = np.zeros(raster_img_size,np.uint8)
    if contours is None:
        return img_mask
    perim_list,interior_list = contours
    cv2.fillPoly(img_mask,perim_list,class_value)
    #cv2.fillPoly(img_mask,interior_list,0)
    return img_mask


def generate_mask_for_image_and_class(raster_size, imageId, class_type, grid_sizes_panda,
                                     wkt_list_pandas):
    xymax = _get_xmax_ymin(grid_sizes_panda,imageId)
    polygon_list = _get_polygon_list(wkt_list_pandas,imageId,class_type)
    contours = _get_and_convert_contours(polygon_list,raster_size,xymax)
    mask = _plot_mask_from_contours(raster_size,contours,class_type)
    return mask


inDir = '../input'


# read the training data from train_wkt_v4.csv
df = pd.read_csv(inDir + '/train_wkt_v4.csv')

# grid size will also be needed later..
gs = pd.read_csv(inDir + '/grid_sizes.csv', names=['ImageId', 'Xmax', 'Ymin'], skiprows=1)

xmax, ymin, W, H=get_size("6120_2_2")
img_mask = np.zeros((W,H),np.uint8)
for i in range(1,10):
    mask = generate_mask_for_image_and_class((W,H),"6120_2_2",i,gs,df)
cv2.imwrite("mask.png",mask)
#np.savetxt('maximums.txt', mask.astype(int),delimt)# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in 

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import cv2
from shapely.wkt import loads as wkt_loads
import tifffile as tiff


# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory

from subprocess import check_output
print(check_output(["ls", "../input"]).decode("utf8"))

# Any results you write to the current directory are saved as output.

# The code is for python 2.7. Parts of it are taken from other posts/kernels.
# Good luck!


def _get_image_names(base_path, imageId):
    '''
    Get the names of the tiff files
    '''
    d = {'3': path.join(base_path,'three_band/{}.tif'.format(imageId)),             # (3, 3348, 3403)
         'A': path.join(base_path,'sixteen_band/{}_A.tif'.format(imageId)),         # (8, 134, 137)
         'M': path.join(base_path,'sixteen_band/{}_M.tif'.format(imageId)),         # (8, 837, 851)
         'P': path.join(base_path,'sixteen_band/{}_P.tif'.format(imageId)),         # (3348, 3403)
         }
    return d
    
def get_images(imageId, img_key = None):
    '''
    Load images correspoding to imageId

    Parameters
    ----------
    imageId : str
        imageId as used in grid_size.csv
    img_key : {None, '3', 'A', 'M', 'P'}, optional
        Specify this to load single image
        None loads all images and returns in a dict
        '3' loads image from three_band/
        'A' loads '_A' image from sixteen_band/
        'M' loads '_M' image from sixteen_band/
        'P' loads '_P' image from sixteen_band/

    Returns
    -------
    images : dict
        A dict of image data from TIFF files as numpy array
    '''
    img_names = get_image_names(imageId)
    images = dict()
    if img_key is None:
        for k in img_names.keys():
            images[k] = tiff.imread(img_names[k])
    else:
        images[img_key] = tiff.imread(img_names[img_key])
    return images
    
def get_image_names(imageId):
    '''
    Get the names of the tiff files
    '''
    d = {'3': '{}/three_band/{}.tif'.format(inDir, imageId),
         'A': '{}/sixteen_band/{}_A.tif'.format(inDir, imageId),
         'M': '{}/sixteen_band/{}_M.tif'.format(inDir, imageId),
         'P': '{}/sixteen_band/{}_P.tif'.format(inDir, imageId),
         }
    return d

    
def get_size(imageId):
    """
    Get the grid size of the image

    Parameters
    ----------
    imageId : str
        imageId as used in grid_size.csv
    """
    xmax, ymin = gs[gs.ImageId == imageId].iloc[0,1:].astype(float)
    W, H = get_images(imageId, '3')['3'].shape[1:]
    return (xmax, ymin, W, H)



def _convert_coordinates_to_raster(coords, img_size, xymax):
    Xmax,Ymax = xymax
    H,W = img_size
    W1 = 1.0*W*W/(W+1)
    H1 = 1.0*H*H/(H+1)
    xf = W1/Xmax
    yf = H1/Ymax
    coords[:,1] *= yf
    coords[:,0] *= xf
    coords_int = np.round(coords).astype(np.int32)
    return coords_int


def _get_xmax_ymin(grid_sizes_panda, imageId):
    xmax, ymin = grid_sizes_panda[grid_sizes_panda.ImageId == imageId].iloc[0,1:].astype(float)
    return (xmax,ymin)


def _get_polygon_list(wkt_list_pandas, imageId, cType):
    df_image = wkt_list_pandas[wkt_list_pandas.ImageId == imageId]
    multipoly_def = df_image[df_image.ClassType == cType].MultipolygonWKT
    polygonList = None
    if len(multipoly_def) > 0:
        assert len(multipoly_def) == 1
        polygonList = wkt_loads(multipoly_def.values[0])
    return polygonList


def _get_and_convert_contours(polygonList, raster_img_size, xymax):
    perim_list = []
    interior_list = []
    if polygonList is None:
        return None
    for k in range(len(polygonList)):
        poly = polygonList[k]
        perim = np.array(list(poly.exterior.coords))
        perim_c = _convert_coordinates_to_raster(perim, raster_img_size, xymax)
        perim_list.append(perim_c)
        for pi in poly.interiors:
            interior = np.array(list(pi.coords))
            interior_c = _convert_coordinates_to_raster(interior, raster_img_size, xymax)
            interior_list.append(interior_c)
    return perim_list,interior_list


def _plot_mask_from_contours(raster_img_size, contours, class_value):
    #img_mask = np.zeros(raster_img_size,np.uint8)
    if contours is None:
        return img_mask
    perim_list,interior_list = contours
    cv2.fillPoly(img_mask,perim_list,class_value)
    #cv2.fillPoly(img_mask,interior_list,0)
    return img_mask


def generate_mask_for_image_and_class(raster_size, imageId, class_type, grid_sizes_panda,
                                     wkt_list_pandas):
    xymax = _get_xmax_ymin(grid_sizes_panda,imageId)
    polygon_list = _get_polygon_list(wkt_list_pandas,imageId,class_type)
    contours = _get_and_convert_contours(polygon_list,raster_size,xymax)
    mask = _plot_mask_from_contours(raster_size,contours,class_type)
    return mask


inDir = '../input'


# read the training data from train_wkt_v4.csv
df = pd.read_csv(inDir + '/train_wkt_v4.csv')

# grid size will also be needed later..
gs = pd.read_csv(inDir + '/grid_sizes.csv', names=['ImageId', 'Xmax', 'Ymin'], skiprows=1)

xmax, ymin, W, H=get_size("6170_4_1")
img_mask = np.zeros((W,H),np.uint8)
for i in range(1,10):
    mask = generate_mask_for_image_and_class((W,H),"6170_4_1",i,gs,df)
cv2.imwrite("6170_4_1mask.png",mask)
#np.savetxt('maximums.txt', mask.astype(int),delimt)