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import cv2
import numpy as np
import pandas as pd
import random
from functools import singledispatchmethod
import math
import os
from sympy import false
import config
class MapIn:
# thickness configs
tree_safe_dist = config.TREE_SAFE_DIST
facility_safe_dist = config.FACILITY_SAFE_DIST
# parcel min area
parcel_minimum_area = config.AXIS_MIN_AREA
# ratio of access to boundry to stop halving
access_ratio = config.ACCESS_RATIO
"""
map class to handle raw inputs
RGB (round access, green factor, boundary)
Background is white by deafult
Black shows fixed facilities
"""
@singledispatchmethod
def __init__(self) -> None:
assert False, 'bad input'
@__init__.register(str)
def _first__(self,src:str,src_block:str,src_ff:str,parcel_cnt:int,arch_choice:config.ArchStyles,map_id:int) -> None:
self.roud_thickness = config.ROAD_SIZE_MAX
self.map_id = map_id
self.frame = cv2.imread(src)
self.frame_shape = self.frame.shape
self.arch_choice = arch_choice
self.parcel_cnt = parcel_cnt
self.centers = (int(self.frame_shape[0]/2),int(self.frame_shape[1]/2))
self.trees_mask, self.fixed_f_mask,self.access_mask, self.boundry_mask = self.create_masks()
self.trees_binary_mask = cv2.threshold(self.trees_mask, 127, 255, cv2.THRESH_BINARY)[1]
# read block mask
self.block_mask = cv2.imread(src_block)
self.block_mask = cv2.cvtColor(self.block_mask,cv2.COLOR_BGR2GRAY)
# read facility filled mask
self.facility_filled_mask = cv2.imread(src_ff)
self.facility_filled_mask = cv2.cvtColor(self.facility_filled_mask,cv2.COLOR_BGR2GRAY)
self.print_report()
# Axis maps initialization
@__init__.register(np.ndarray)
def _second__(self,split_mask:np.ndarray,parent_map,line_mask:np.ndarray,map_id:int,dir:int,line_p) -> None:
self.line_p = line_p
# self.axis_center = parent_map.axis_center
self.split_mask = split_mask
self.dir = dir # 0 up 1 down
self.roud_thickness = config.ROAD_SIZE_MAX - config.ROAD_STEP*int(math.log(map_id+1,2))
if self.roud_thickness <= config.ROAD_STEP: self.roud_thickness=config.ROAD_SIZE_MIN
config.log(f"roud thickness:{self.roud_thickness} map_id:{map_id}")
self.map_id = map_id
# split_mask_3d = np.zeros((self.frame_shape))
# self.frame = parent_map.frame & split_mask
split_3d_mask = np.zeros(parent_map.frame_shape, dtype=np.uint8)
split_3d_mask[:,:,:] = split_mask[:,:,np.newaxis]
self.frame = parent_map.frame & split_3d_mask
self.frame_shape = self.frame.shape
self.arch_choice = parent_map.arch_choice
self.centers = (int(self.frame_shape[0]/2),int(self.frame_shape[1]/2))
self.trees_mask = parent_map.trees_mask & split_mask
self.trees_binary_mask = parent_map.trees_binary_mask & split_mask
self.fixed_f_mask = parent_map.fixed_f_mask & split_mask
self.boundry_mask = parent_map.boundry_mask & split_mask
self.old_boundry_mask = parent_map.boundry_mask & split_mask
self.block_mask = parent_map.block_mask & split_mask
self.facility_filled_mask = parent_map.facility_filled_mask & split_mask
# add new line to access
new_access_line = self.block_mask & line_mask
self.access_mask = parent_map.access_mask & split_mask
self.access_mask = self.access_mask | new_access_line
# add new line to boundries
self.boundry_mask = self.boundry_mask | new_access_line
self.new_access_line = new_access_line
if map_id > 2:
self.parent_access_line = parent_map.new_access_line
self.parent_line_p = parent_map.line_p
else:
self.parent_access_line = self.new_access_line
self.parent_line_p = self.line_p
self.save_map()
self.print_report()
# Parcels initilization
@__init__.register(int)
def _third__(self,parcel_id:int,split_mask:np.ndarray,parent_map,parcel_area,lines_points_tup,parcel_type) -> None:
self.dir = parent_map.dir
self.parent_line_p = parent_map.parent_line_p
self.line_p = parent_map.line_p
self.parent_access_line = parent_map.parent_access_line & split_mask
self.access_line = parent_map.new_access_line & split_mask
self.parcel_id = parcel_id
self.curr_size = parcel_area
self.bounding_lines = lines_points_tup
self.parcel_type = parcel_type
self.map_id = parent_map.map_id
split_3d_mask = np.zeros(parent_map.frame_shape, dtype=np.uint8)
split_3d_mask[:,:,:] = split_mask[:,:,np.newaxis]
self.frame = parent_map.frame & split_3d_mask
self.frame_shape = self.frame.shape
self.arch_choice = parent_map.arch_choice
self.trees_mask = parent_map.trees_mask & split_mask
self.trees_binary_mask = parent_map.trees_binary_mask & split_mask
self.fixed_f_mask = parent_map.fixed_f_mask & split_mask
self.boundry_mask = parent_map.boundry_mask & split_mask
self.block_mask = parent_map.block_mask & split_mask
self.facility_filled_mask = parent_map.facility_filled_mask & split_mask
# make center of new parcel
block_mask = self.block_mask.astype(np.uint8)
contours, _ = cv2.findContours(block_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
cnts = sorted(contours, key=cv2.contourArea, reverse=True)
M = cv2.moments(cnts[0])
cX = int(M["m10"] / M["m00"])
cY = int(M["m01"] / M["m00"])
self.parcel_center = (cY,cX)
# self.save_map()
self.print_report()
def print_report(self):
config.log(f"Map {self.map_id} Area : {np.sum(self.block_mask)/255}")
config.log(f"Map {self.map_id} Tree Area : {np.sum(self.trees_binary_mask)/255}")
config.log(f"Map {self.map_id} Fixed-Facility Area : {np.sum(self.facility_filled_mask)/255}")
config.log(f"Map {self.map_id} Sparse Area : {np.sum(self.block_mask & np.bitwise_not(self.facility_filled_mask) & np.bitwise_not(self.trees_binary_mask))/255}")
def set_map_axis_center(self,point):
self.axis_center = point
def set_line_point(self,point:tuple):
self.line_points = point
def save_map(self) -> None:
if config.WRITE_UNNECESSARY:
cv2.imwrite(f'outputs/map{self.map_id}.bmp',self.frame)
cv2.imwrite(f'outputs/access_mask{self.map_id}.bmp',self.access_mask)
cv2.imwrite(f'outputs/boundry_mask{self.map_id}.bmp',self.boundry_mask)
def create_masks(self) -> tuple:
res = []
img_re = self.frame.reshape(-1,3)
df = pd.DataFrame(img_re,columns=['b','g','r'])
df['r'].astype(np.uint8)
df['g'].astype(np.uint8)
df['b'].astype(np.uint8)
indx_trees = df.apply(lambda x: x.b==0 and 0<x.g<=255 and x.r==0, axis=1)
df_trees = df.copy()
df_trees[np.logical_not(indx_trees)] = [0,0,0]
# df_trees[indx_trees] = [255,255,255]
out = df_trees.values.reshape(self.frame_shape)
out = out.astype(np.uint8)
out[:,:,0] = 0
out[:,:,2] = 0
out = cv2.threshold(out, 127, 255, cv2.THRESH_BINARY)[1][:,:,1]
res.append(out)
cv2.imwrite('outputs/tree_mask.bmp',out)
indx_fixed_fac = df.apply(lambda x: x.b==0 and x.g==0 and x.r==0, axis=1)
df_ff = df.copy()
df_ff[np.logical_not(indx_fixed_fac)] = [0,0,0]
df_ff[indx_fixed_fac] = [255,255,255]
out = df_ff.values.reshape(self.frame_shape)
out = out.astype(np.uint8)
out = cv2.cvtColor(out,cv2.COLOR_BGR2GRAY)
res.append(out)
cv2.imwrite('outputs/facility_mask.bmp',out)
indx_access = df.apply(lambda x: x.g==0 and x.r==255, axis=1)
df_ac = df.copy()
df_ac[np.logical_not(indx_access)] = [0,0,0]
df_ac[indx_access] = [255,255,255]
out = df_ac.values.reshape(self.frame_shape)
out = out.astype(np.uint8)
out = cv2.cvtColor(out,cv2.COLOR_BGR2GRAY)
res.append(out)
cv2.imwrite('outputs/access_mask.bmp',out)
indx_boundry = df.apply(lambda x: x.b==255 and x.g==0, axis=1)
df_b = df.copy()
df_b[np.logical_not(indx_boundry)] = [0,0,0]
df_b[indx_boundry] = [255,255,255]
out = df_b.values.reshape(self.frame_shape)
out = out.astype(np.uint8)
out = cv2.cvtColor(out,cv2.COLOR_BGR2GRAY)
res.append(out)
cv2.imwrite('outputs/boundary_mask.bmp',out)
return tuple(res)
def correct_input(self) -> None:
img_re = self.frame.reshape(-1,3)
df = pd.DataFrame(img_re,columns=['b','g','r'])
df['r'].astype(np.uint8)
df['g'].astype(np.uint8)
df['b'].astype(np.uint8)
# ----set tree values to random
random.seed(13)
df = df.apply(lambda x: [0,random.randint(1,255),0] if x['b']==0 and x['g']==255 and x['r']==0 else x,axis=1)
# ----convert white to black
# indx = df.apply(lambda x: x['b']==255 and x['g']==255 and x['r']==255,axis=1)
# df[indx] = [0,0,0]
# ----set rgb(0,255,255) to rgb(255,0,255)
# indx = df.apply(lambda x: x['b']==255 and x['g']==255 and x['r']==0,axis=1)
# df[indx] = [255,0,255]
# ----save output
out = df.values.reshape(self.frame_shape)
out = out.astype(np.uint8)
cv2.imwrite('outputs/kan_pre.bmp',out)
self.frame = out
# ----print
# cv2.imshow("kan_pre_out", out)
# cv2.waitKey(0)
# cv2.destroyAllWindows()
"""
returns above the line mask and below the line mask
"""
def line_split_mask_maker(self,p0:tuple,p1:tuple):
# points are (y,x) oriented
img_pixels = self.frame_shape[0]*self.frame_shape[1]
img_x = self.frame_shape[1]
# numpy image is (y*x*3)
# !! unit16 may not be enough
y_index = (np.arange(img_pixels).reshape(self.frame_shape[:2])/img_x).astype(np.uint32)
x_index = np.arange(img_pixels).reshape(self.frame_shape[:2])%img_x
if p1[1] == p0[1]:
up_down_line = x_index - p0[1]
else:
slope = (p1[0]-p0[0])/(p1[1]-p0[1])
intercept = p0[0] - (slope*p0[1])
up_down_line = x_index*slope + intercept - y_index
# down part (becareful about center)
down_mask = np.where(up_down_line>=0,255,0).reshape(self.frame_shape[:2])
up_mask = np.where(up_down_line>=0,0,255).reshape(self.frame_shape[:2])
return (up_mask,down_mask)
"""
returns only line mask on main image
"""
def line_mask_maker(self,p0:tuple,p1:tuple):
plain = np.zeros((self.block_mask.shape))
plain = cv2.line(plain,(p0[1],p0[0]),(p1[1],p1[0]),255,2)
return plain.astype(np.uint8)
"""
check whether the half map has a feasible condition
or supports the finishing condtion.
"""
def isfeasible(self):
# check if the part has more than 60% access
access = np.sum(self.access_mask)/255
boundry = np.sum(self.boundry_mask)/255
access_ratio = access/boundry
access_cond = access_ratio<self.access_ratio
# area of part not smaller than standard
block_size = np.sum(self.block_mask)/255
size_cond = block_size>self.parcel_minimum_area
config.log(f'block size:{block_size} access_ratio:{access_ratio} map_id:{self.map_id}')
self.curr_access = access_ratio
self.curr_size = block_size
return access_cond and size_cond
class CVLineThickness:
"""
method selects cv2line arg
depending on the pixel width
"""
@staticmethod
def thickness_solver(desired_thickness):
if desired_thickness == 1:
return 1
if desired_thickness == 2:
# assert false, f"change road size or road step to odd number: {desired_thickness}"
return 2
if desired_thickness == 3:
return 2
if desired_thickness % 2 == 0:
# assert false, f"change road size or road step to odd number: {desired_thickness}"
return desired_thickness - 1
return desired_thickness - 2
class MapOut:
def __init__(self,src:str,lines_axis:list) -> None:
self.img = cv2.imread(src)
self.img_axised = self.img.copy()
self.img_partitioned = None
self.img_built = None
self.img_last = None
self.axis_lines = lines_axis
self.partitioning_lines = None
self.parcels_dic = {}
self.building_masks = None
# export details
self.total_carbon = 0
self.total_trees = 0
self.total_carbon_loss = 0
self.total_cut_tree = 0
self.total_axis_length = 0
self.total_axis_per_block_pr = 0
self.total_num_parcels = 0
self.total_num_parcels_types = {p_type:0 for p_type in config.ParcelType._member_names_}
self.total_sum_ff = 0
def reset_map_for_partitioning(self):
self.total_num_parcels = 0
self.total_num_parcels_types = {p_type:0 for p_type in config.ParcelType._member_names_}
self.total_sum_ff = 0
self.partitioning_lines = None
if self.img_partitioned is not None:
self.img_partitioned = None
self.img_last = self.img_axised.copy()
def reset_map_for_location_finding(self):
self.total_sum_ff = 0
self.building_masks = None
if self.img_built is not None:
self.img_built = None
self.img_last = self.img_partitioned.copy()
def add_partition_report(self,report):
self.total_num_parcels += report['cnt']
report.pop('cnt')
for p_type in report.keys():
self.total_num_parcels_types[p_type] += report[p_type]
def report(self):
self.block_mask = cv2.imread(config.MAIN_MAP_FILLED_BLOCK_MASK)
self.tree_mask = cv2.imread('outputs/tree_mask.bmp')
self.binary_tree_mask = cv2.threshold(self.tree_mask, 127, 255, cv2.THRESH_BINARY)[1]
self.facility_filled_mask = cv2.imread(config.MAIN_MAP_FILLED_F_F_MASK)
# total trees carbon
self.total_carbon = np.sum(self.tree_mask)/3
self.total_trees = np.sum(self.binary_tree_mask)/(255*3)
# calculate tree cut and carbon
self.img_last = self.img_last & self.block_mask
self.img_last = self.img_last.astype(np.uint8)
self.img_mask = cv2.threshold(self.img_last, 127, 255, cv2.THRESH_BINARY)[1]
# omit roads mask and building mask from it
self.roads_mask = cv2.imread('outputs/roads_mask.bmp')
collision3dmask = self.roads_mask
if os.path.exists('outputs/buildings_mask.bmp'):
collision3dmask = collision3dmask | cv2.imread('outputs/buildings_mask.bmp')
if os.path.exists('outputs/partitioning_mask.bmp'):
collision3dmask = collision3dmask | cv2.imread('outputs/partitioning_mask.bmp')
cv2.imwrite('outputs/constructed_mask.bmp', collision3dmask)
self.total_carbon_loss = np.sum(collision3dmask & self.tree_mask)/3
self.total_cut_tree = np.sum(collision3dmask & self.binary_tree_mask)/(255*3)
config.log(f"Total Trees:{self.total_trees} Total Carbon Values:{self.total_carbon}")
config.log(f"Total Cut Trees:{self.total_cut_tree} Total Carbon Loss:{self.total_carbon_loss}")
config.log(f"Total Cut Precentage:{self.total_cut_tree/self.total_trees}")
# calculate axis reports
img_re = self.img_last.reshape(-1,3)
df = pd.DataFrame(img_re,columns=['b','g','r'])
df['r'].astype(np.uint8)
df['g'].astype(np.uint8)
df['b'].astype(np.uint8)
indx_axis = df.apply(lambda x:x.g == 0 and 0<x.r<=255 and x.b == 0, axis=1)
df_axis = df.copy()
df_axis[np.logical_not(indx_axis)] = [0,0,0]
out = df_axis.values.reshape(self.img_last.shape)
out = out.astype(np.uint8)
out = cv2.cvtColor(out,cv2.COLOR_BGR2GRAY)
out = cv2.threshold(out, 1, 255, cv2.THRESH_BINARY)[1]
self.total_axis_length = np.sum(out)/255
self.total_axis_per_block_pr = self.total_axis_length / (np.sum(self.block_mask)/(255*3))
sparse_area = np.sum(self.block_mask & np.bitwise_not(self.facility_filled_mask) & np.bitwise_not(self.binary_tree_mask))/(255*3)
self.total_axis_per_sparse_pr = self.total_axis_length / sparse_area
config.log(f"Total Axis Area:{self.total_axis_length}")
config.log(f"Total Block Area:{np.sum(self.block_mask)/(255*3)}")
config.log(f"Total Sparse Area:{sparse_area}")
config.log(f"Total Axis Per Block Precentage:{self.total_axis_per_block_pr}")
config.log(f"Total Axis Per Sparse Precentage:{self.total_axis_per_sparse_pr}")
# Parcels number plus types
config.log(f"Total Parcels:{self.total_num_parcels}")
config.log(f"Total Parcel types:{self.total_num_parcels_types}")
config.log(f"Total Parcels With FF:{self.total_sum_ff}")
def draw_axis(self):
for line in self.axis_lines:
p0=line[0][0]
p1=line[0][1]
thickness=config.ROAD_SIZE_MAX - config.ROAD_STEP*int(math.log(line[1]+1,2))
if thickness <= config.ROAD_SIZE_MIN:
thickness=config.ROAD_SIZE_MIN
# draw line of axis
self.img_axised = cv2.line(self.img_axised,(p0[1],p0[0]),(p1[1],p1[0]),(0,0,127),CVLineThickness.thickness_solver(thickness+2))
# rotate points
self.img_axised = cv2.line(self.img_axised,(p0[1],p0[0]),(p1[1],p1[0]),(0,0,255),CVLineThickness.thickness_solver(thickness))
cv2.imwrite('outputs/final_axis.bmp',self.img_axised)
# save road lines mask
img_re = self.img_axised.reshape(-1,3).copy()
df = pd.DataFrame(img_re,columns=['b','g','r'])
df['r'].astype(np.uint8)
df['g'].astype(np.uint8)
df['b'].astype(np.uint8)
indx_axis = df.apply(lambda x:x.g == 0 and 0<x.r<=255 and x.b == 0, axis=1)
df[np.logical_not(indx_axis)] = [0,0,0]
out = df.values.reshape(self.img_axised.shape)
out = out.astype(np.uint8)
out = cv2.cvtColor(out,cv2.COLOR_BGR2GRAY)
out = cv2.threshold(out, 1, 255, cv2.THRESH_BINARY)[1]
self.img_last = self.img_axised.copy()
cv2.imwrite('outputs/roads_mask.bmp', out)
def draw_partitions(self,iteration:int,map:MapIn,lines_parcels:list):
map_id = map.map_id
if lines_parcels is not None:
self.parcels_dic[map_id] = lines_parcels
lines_mask = np.zeros(self.img_last.shape, dtype=np.uint8)
for line in lines_parcels:
p0=line[0]
p1=line[1]
# rotate points
lines_mask = cv2.line(lines_mask,(p0[1],p0[0]),(p1[1],p1[0]),(120,120,120),1)
split_3d_mask = np.zeros(self.img_last.shape, dtype=np.uint8)
split_3d_mask[:,:,:] = map.block_mask[:,:,np.newaxis]
lines_mask = lines_mask & split_3d_mask
self.img_partitioned = self.img_last.astype(np.uint8) & np.bitwise_not(lines_mask)
# write partitioning mask
lines_mask = np.where(lines_mask>0,(255,255,255), (0,0,0))
if self.partitioning_lines is not None:
self.partitioning_lines |= lines_mask
else:
self.partitioning_lines = lines_mask
if config.WRITE_UNNECESSARY:
cv2.imwrite(f'outputs/final_map_{iteration}_{map_id}.bmp',self.img_partitioned)
self.img_last = self.img_partitioned.copy()
def draw_partitioning_results(self):
cv2.imwrite(f'outputs/partitioning_mask.bmp',self.partitioning_lines)
cv2.imwrite(f'outputs/final_map_partitioning.bmp',self.img_partitioned)
def draw_building(self,building_mask,iteration,map_id,parcel_id,has_building,parcel_type,block_mask):
color3d_mask = np.zeros(self.img_last.shape, dtype=np.uint8)
color3d_mask[:,:,:] = block_mask[:,:,np.newaxis]
if has_building:
self.total_sum_ff += 1
color3d_mask = np.where(color3d_mask>0,(100,100,100),(255,255,255))
elif parcel_type == config.ParcelType.O:
color3d_mask = np.where(color3d_mask>0,(0,255,255),(255,255,255))
elif parcel_type == config.ParcelType.A:
color3d_mask = np.where(color3d_mask>0,(51,255,255),(255,255,255))
elif parcel_type == config.ParcelType.B:
color3d_mask = np.where(color3d_mask>0,(102,255,255),(255,255,255))
elif parcel_type == config.ParcelType.C:
color3d_mask = np.where(color3d_mask>0,(153,255,255),(255,255,255))
elif parcel_type == config.ParcelType.U:
color3d_mask = np.where(color3d_mask>0,(40,0,255),(255,255,255))
build3d_mask = np.zeros(self.img.shape, dtype=np.uint8)
build3d_mask[:,:,:] = building_mask[:,:,np.newaxis]
if self.building_masks is not None:
self.building_masks &= build3d_mask
self.img_last &= self.img_partitioned & build3d_mask
self.img_built &= self.img_partitioned & build3d_mask & color3d_mask
else:
self.building_masks = build3d_mask
self.img_last = self.img_partitioned & build3d_mask
self.img_built = self.img_partitioned & build3d_mask & color3d_mask
if config.WRITE_UNNECESSARY:
cv2.imwrite(f'outputs/final_map_{iteration}_{map_id}_{parcel_id}.bmp',self.img_built)
def draw_building_results(self):
cv2.imwrite(f'outputs/buildings_mask.bmp',np.bitwise_not(self.building_masks))
cv2.imwrite(f'outputs/final_map_location_finding.bmp',self.img_built)
def draw_collision(self):
trees_mask = cv2.imread('outputs/tree_mask.bmp')
fixed_facility_mask = cv2.imread('outputs/facility_mask.bmp')
roads_mask = cv2.imread('outputs/roads_mask.bmp')
collide_mask = roads_mask
if os.path.exists('outputs/buildings_mask.bmp'):
collide_mask |= cv2.imread('outputs/buildings_mask.bmp')
if os.path.exists('outputs/partitioning_mask.bmp'):
collide_mask |= cv2.imread('outputs/partitioning_mask.bmp')
# change here when building mask is av
collision3dmask = trees_mask | fixed_facility_mask
collision3dmask = collide_mask & collision3dmask
img = self.img_last.copy()
pixels = [100,50,100]*int(len(img[collision3dmask>0])/3)
img[collision3dmask>0] = pixels
cv2.imwrite(f'outputs/collision_map.bmp',img) |