#! /usr/bin/python3
# Last edited on 2021-03-02 12:00:42 by jstolfi

# Reads a bunch of egg images and adusts a mean contour.

import numpy as np
import cv2 as cv
from matplotlib import pyplot as plt
import sys
import math

show_in = False
show_gr = False
show_hg = False
show_tr = False

imdir = sys.argv[1]
imname = sys.argv[2]

# Read the image
img_in = cv.imread('in/%s/%s.png' % (imdir,imname))
if show_in:
  cv.imshow('img_in',img_in)
  cv.waitKey(0)

# Compute the histogram of the red channel:
hist_in = cv.calcHist([img_in], [0],  None, [512], [0,512])
if show_hg:
  aplt.plot(hist_in)
  plt.show()

# Convert image to grayscale:
img_gr = cv.cvtColor(img_in, cv.COLOR_BGR2GRAY)
img_gr = cv.blur(img_gr, (3,3))
if show_gr:
  cv.imshow('img_gr',img_gr)
  cv.waitKey(0)

# Threshold the image:
ret, img_tr = cv.threshold(img_gr, 190, 255, cv.THRESH_BINARY_INV)
if show_tr:
  cv.imshow('img_tr',img_tr)
  cv.waitKey(0)

# Extract the contour
contours, hierarchy = cv.findContours(img_tr, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_NONE)
nc = len(contours)
sys.stderr.write("found %d contours\n" % nc)
assert nc == 1

# Write the points of the contour: 
wr = open('out/%s-pts.txt' % imname, 'w')
szy, szx = img_tr.shape
pts = []
for k in range(nc):
  ctk = contours[k]
  npk = len(ctk)
  sys.stderr.write("  contour %d has %d points\n" % (k,npk))
  for i in range(npk):
    ptki = ctk[i]
    # sys.stderr.write("    ct[%d][%d] = %s\n" % (k,i,str(ptki)))
    xki = ptki[0][0]
    yki = szy - ptki[0][1]
    wr.write("%6d %6d %6d\n" % (i,xki,yki))
    pts.append((xki,yki))
  wr.write("\n")
wr.close()