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#%%

from timeit import default_timer as timer

import numpy as np

import matplotlib.pyplot as plt

import cv2 as cv

# import pickle

# import bz2

# import lzma

# import sys

from PIL import Image, ImageFilter

# import skimage.morphology as morph

import functions as f

import importlib as imp

# %% takes approximatly half the length of the video to decompress

# A_video = f.import_lzma('cameraA_test1')

# B_video = f.import_lzma('cameraB_test1')

A_video_in, B_video_in = f.import_bz2('test_photos1')

frame = 4

imp.reload(f)

A_video = f.gauss_blur(A_video_in, 2)

B_video = f.gauss_blur(B_video_in, 2)

f.compare_frames(A_video, B_video, frame, s_title='Video frames')

# %%

# A_background = f.ref_image(A_video)

# B_background = f.ref_image(B_video)

A_background = A_video[:,:,0:1]

B_background = B_video[:,:,0:1]

f.compare_frames(A_background, B_background, 0, s_title='Reference image')

imp.reload(f)

A_diff = f.difference(A_video, A_background)

B_diff = f.difference(B_video, B_background)

f.compare_frames(A_diff, B_diff, frame, s_title='Difference')

nframes = (np.shape(A_video)[2], np.shape(A_video)[2])

print(nframes)

imp.reload(f)

thresh_val = 0.3

# A_thresh = f.threshold(A_diff[:,:,frame:frame+1], thresh_val)

A_thresh = f.threshold(A_diff, 0.15)

B_thresh = f.threshold(B_diff, 0.2)

# f.compare_frames(A_thresh, A_diff[:,:,frame:frame+1], 0)

f.compare_frames(A_thresh, B_thresh, frame, axis='on', s_title = 'Thresholded')

def plot(frame, title=np.nan):

plt.imshow(frame, cmap='gray')

radius = 14

A_closed = f.closing_disk(A_thresh, radius)

B_closed = f.closing_disk(B_thresh, radius)

# %%

# # plot(A_closed)

# for i in range(0,9):

# f.compare_frames(A_closed, B_closed, i)

# %%

A_xy = f.centre_points(A_closed)

B_xy = f.centre_points(B_closed)

i = -7

plt.figure()

plt.imshow(A_closed[:,:,i], cmap='gray'); plt.axis('off')

plt.scatter(A_xy[0,i],A_xy[1,i], color='r', s=20, marker='x')

# plt.axis([A_xy[0]-40, A_xy[0]+40, A_xy[1]-40, A_xy[1]+40])

plt.xlim([A_xy[0,i]-40, A_xy[0,i]+40])

plt.ylim([A_xy[1,i]-40, A_xy[1,i]+40])

# %%

imp.reload(f)

A_img = f.open_img('IMG_0795.jpg')

# A_img=f.open_img('IMG_3014.jpg')

# f.calib_honing(A_img, 100, 100)

f.calib_count(A_img, (100,100))

# %%

imp.reload(f)

image_save = f.visualise(B_video_in, B_xy)

f.vis_player(image_save, 1000)

# %% Angles

calib = f.import_bz2('angles2')

A_fsize = np.shape(A_closed)[0:2] # rows[0] is y, columns[1] is x

B_fsize = np.shape(B_closed)[0:2]

Aang_x = (A_xy[0,:]-A_fsize[1]/2)*calib[0,0]

Aang_y = (A_xy[1,:]-A_fsize[0]/2)*calib[0,1]

Bang_x = (B_xy[0,:]-B_fsize[1]/2)*calib[0,0]

Bang_y = (B_xy[1,:]-B_fsize[0]/2)*calib[0,0]

angles = np.vstack((Aang_x, Aang_y, Bang_x, Bang_y))

plt.plot(np.repeat(np.expand_dims(range(0,9), 1), angles.shape[0], 1), angles.T)

# %%

f.export_bz2('test_photos_angles2', angles)

# %%

add1 = np.sum(B_closed,2)

plt.figure()

plt.imshow(add1)

# %%

AAtheta = Bang_x[1]

r = np.linspace(0,100, 100)

x = r*np.cos(AAtheta)

y = r*np.sin(AAtheta)

# plt.figure()

# plt.plot(x,y)

AAdegrees = np.degrees(Bang_x)

print(AAdegrees)

AAcalib_deg = np.degrees(calib)

print(AAcalib_deg)

AAx1 = A_xy[1]

# %%