import numpy
from investigators.visuals import cv2  # package includes a fallback cv2
from investigators.visuals import TemplateFinder, ProportionalRegion, Grid
from investigators.visuals import ImageIdentifier, TankLevel

# Show the starting screenshot
screenshot = cv2.imread('screenshot 1280x800, game 800x600.png')
cv2.imshow('screenshot', screenshot)
cv2.waitKey()

# Use TemplateFinder to locate and show an image of the game on the screen
template = cv2.imread('game 640x480.png')
mask = cv2.imread('game mask 640x480.png')
possible_sizes = (600, 800), (240, 320)
finder = TemplateFinder(template, sizes=possible_sizes, mask=mask,
                        scale_for_speed=0.25)
t, l, b, r = finder.locate_in(screenshot)
game = screenshot[t:b, l:r]
cv2.destroyWindow('screenshot')
cv2.imshow('extracted game', game)
cv2.waitKey()

# Use ProportionalRegion to isolate the board within the game
tlbr_proportions = (80.0 / 480, 135.0 / 640, 450.0 / 480, 504.0 / 640)
proportional = ProportionalRegion(tlbr_proportions)
t, l, b, r = proportional.region_in(game)
board = game[t:b, l:r]
cv2.imshow('extracted board', board)
cv2.waitKey()

# Use Grid to split the grid into image cells. record only the first four
board_dimensions = 8, 8
tile_padding = (0.1, 0.1, 0.1, 0.1)  # just for demonstration
grid = Grid(board_dimensions, tile_padding)
first_four_tiles = list()
for grid_p, cell_borders in grid.borders_by_grid_position(board):
    if grid_p in ((0, 0), (0, 1), (0, 2), (0, 3)):
        t, l, b, r = cell_borders
        first_four_tiles.append(board[t:b, l:r])

# Use ImageIdentifier to identify each of the first four tiles
identifier = ImageIdentifier({'red': cv2.imread('r.png'),
                              'blue': cv2.imread('b.png'),
                              'coins': cv2.imread('m.png'),
                              'skull': cv2.imread('s.png')})
for tile in first_four_tiles:
    name = identifier.identify(tile)
    cv2.putText(tile, name, (2, 20), 0, 0.6, (255, 255, 255), 2)
    cv2.imshow('identified original tile', tile)
    cv2.waitKey()
cv2.destroyWindow('identified original tile')
cv2.destroyWindow('extracted board')

# Use ProportionalRegion to isolate the player's health within the game
tlbr_proportions = (76.0 / 480, 8.0 / 640, 87.0 / 480, 116.0 / 640)
proportional = ProportionalRegion(tlbr_proportions)
t, l, b, r = proportional.region_in(game)
health = game[t:b, l:r]

# Use TankLevel to measure how full the health bar is
health_color = (4, 4, 110)
empty_health_color = (15, 15, 50)
ignore_color = (0, 0, 0)
tank_level = TankLevel(health_color, empty_health_color, ignore_color)
health = numpy.rot90(health)  # make it upright for the TankLevel measurement
health_pcnt = 100.0 * tank_level.how_full(health)
health_string = '~{:2.0f}%'.format(health_pcnt)
health = numpy.rot90(health, -1)  # rotate and enlarge for display
h, w = health.shape[0:2]
health = cv2.resize(health, (w * 3, h * 3), interpolation=cv2.INTER_CUBIC)
cv2.putText(health, health_string, (25, 25), 0, 0.8, (0, 255, 0), 2)
cv2.imshow('player health bar', health)
cv2.waitKey()

# Use ProportionalRegion to isolate the extra actions area within the game
tlbr_proportions = (120.0 / 960, 1100.0 / 1280, 160.0 / 960, 1220.0 / 1280)
proportional = ProportionalRegion(tlbr_proportions)
t, l, b, r = proportional.region_in(game)
token_region = game[t:b, l:r]

# Use TemplateFinder (multiple) to check for extra actions
token_template = cv2.imread('extra_turn_token.png')
possible_sizes = (17, 14),
finder = TemplateFinder(token_template, sizes=possible_sizes,
                        acceptable_threshold=0.1, immediate_threshold=0.1)
found_tokens = finder.locate_multiple_in(token_region)
for borders in found_tokens:
    t, l, b, r = borders
    cv2.rectangle(token_region, (l, t), (r, b), (255, 255, 255), 2)
h, w = token_region.shape[0:2]
token_region = cv2.resize(token_region, (w * 3, h * 3),
                          interpolation=cv2.INTER_CUBIC)
cv2.imshow('extra actions', token_region)
cv2.waitKey()

# clean up
cv2.destroyAllWindows()