efish_tracking/etrack/distance_calibration.py

from multiprocessing import allow_connection_pickling
from turtle import left
from xml.dom.expatbuilder import FILTER_ACCEPT
from cv2 import MARKER_TRIANGLE_UP, calibrationMatrixValues, mean, threshold
import matplotlib.pyplot as plt 
import numpy as np
import cv2
import os
import sys
from IPython import embed
from etrack import MarkerTask, ImageMarker
from calibration_functions import crop_frame, threshold_crossings, checkerboard_position, filter_data


class DistanceCalibration():

    def __init__(self, file_name, frame_number, x_0=154, y_0=1318, cam_dist=1.36, width=1.35, height=0.805, width_pixel=1900, height_pixel=200, checkerboard_width=0.24, checkerboard_height=0.18, 
    checkerboard_width_pixel=500, checkerboard_height_pixel=350, rectangle_width=0.024, rectangle_height=0.0225, rectangle_width_pixel=100, rectangle_height_pixel=90, 
    rectangle_count_width=9, rectangle_count_height=7) -> None: 
        super().__init__()
        # aktualisieren
        """Calibration of the dimensions of the tank. Conversion of pixel into meter. Width refers to the "x-axis", height to the "y-axis" of the tank.
        
        Args:
            file_name (_type_): _description_
            x_0 (int, optional): X-value of the "origin" of the tank. Defaults to 0.
            y_0 (int, optional): Y-value of the "origin" of the tank. Defaults to 0.
            cam_dist (int, optional): Distance of camera lense to tank floor. Defaults to 1.36.
            width (int, optional): Width in meter from one lightened corner of the tank to the other. Defaults to 1.35.
            height (int, optional): Height in meter from one lightened corner of the tank to the other. Defaults to 1.35.
            width_pixel (int, optional): Width in pixel from one lightened corner of the tank to the other. Defaults to 1975.
            height_pixel (int, optional): Height in pixel from one lightened corner of the tank to the other.  Defaults to 1375.
            rectangle_width (float, optional): Width of one black or corresponding white rectangle of the checkerboard. Defaults to 0.024.
            rectangle_height (float, optional): Height of one black or corresponding white rectangle of the checkerboard. Defaults to 0.0225.
            rectangle_count_width (int, optional): Number of black rectangles over the width of the whole checkerboard. Defaults to 9.
            rectangle_count_height (int, optional): Number of black rectangles over the height of the whole checkerboard. Defaults to 7.
        """
        
        self._file_name = file_name
        self._x_0 = x_0
        self._y_0 = y_0
        self._width_pix = width_pixel
        self._height_pix = height_pixel
        self._cam_dist = cam_dist
        self._width = width
        self._height = height
        self._cb_width = checkerboard_width
        self._cb_height = checkerboard_height
        self._cb_width_pix = checkerboard_width_pixel
        self._cb_height_pix = checkerboard_height_pixel
        self._rect_width = rectangle_width
        self._rect_height = rectangle_height
        self._x_factor = self.width / self.width_pix   # m/pix
        self._y_factor = self.height / self.height_pix # m/pix

        # self.mark_crop_positions
        # self.threshold_crossings
        # self.checkerboard_position
        # self.filter_data

    @property
    def x_0(self):
        return self._x_0
    
    @x_0.setter
    def x_0(self, value):
        self._x_0 = value

    @property
    def y_0(self):
        return self._y_0

    @y_0.setter
    def y_0(self, value):
        self._y_0 = value

    @property
    def cam_dist(self):
        return self._cam_dist
    
    @property
    def width(self):
        return self._width
    
    @width.setter
    def width(self, value):
        self._width = value
    
    @property
    def height(self):
        return self._height
    
    @height.setter
    def height(self, value):
        self._height = value
    
    @property
    def width_pix(self):
        return self._width_pix

    @width_pix.setter
    def width_pix(self, value):
        self._width_pix = value

    @property
    def height_pix(self):
        return self._height_pix
    
    @height_pix.setter
    def height_pix(self, value):
        self._height_pix_ = value

    @property
    def cb_width(self):
        return self._cb_width
    
    @cb_width.setter
    def cb_width(self, value):
        self._cb_width = value
    
    @property
    def cb_height(self):
        return self._cb_height
    
    @cb_height.setter
    def cb_height(self, value):
        self._cb_height = value

    @property
    def x_factor(self):
        return self._x_factor

    @property
    def y_factor(self):
        return self._y_factor       


    def mark_crop_positions(self):
        task = MarkerTask("crop area", ["bottom left corner", "top left corner", "top right corner", "bottom right corner"], "Mark crop area")
        im = ImageMarker([task])
        
        marker_positions = im.mark_movie(file_name, frame_number)
        print(marker_positions)

        np.save('marker_positions', marker_positions)

        return marker_positions


    def detect_checkerboard(self, filename, frame_number, marker_positions):
        
        if not os.path.exists(filename):
            raise IOError("file %s does not exist!" % filename)
        video = cv2.VideoCapture()
        video.open(filename)
        frame_counter = 0
        success = True
        frame = None
        
        x_0 = self._x_0
        y_0 = self._y_0
        width_pix = self._width_pix
        height_pix = self._height_pix

        while success and frame_counter <= frame_number:    # iterating until frame_counter == frame_number --> success (True)
            print("Reading frame: %i" % frame_counter, end="\r")
            success, frame = video.read()
            frame_counter += 1

        marker_positions = np.load('marker_positions.npy', allow_pickle=True)   # load saved numpy marker positions file
        bottom_left_marker = marker_positions[0]['bottom left corner']
        bottom_right_marker= marker_positions[0]['bottom right corner']
        top_left_marker = marker_positions[0]['top left corner']
        top_right_marker = marker_positions[0]['top right corner']

        # care: y-axis is inverted, top values are low, bottom values are high

        frame_width, frame_height, diff_width, diff_height, _, _ = crop_frame(frame, marker_positions)   # crop frame to given marker positions
        
        thresh_fact = 7    # factor by which the min/max is divided to calculate the upper and lower thresholds 
        
        # filtering/smoothing of data using kernel with n datapoints
        kernel = 4
        diff_width = filter_data(diff_width, n=kernel)  # for widht (x-axis)
        diff_height = filter_data(diff_height, n=kernel)    # for height (y-axis)

        # input data is derivation of color values of frame
        lci_width, uci_width = threshold_crossings(diff_width, threshold_factor=thresh_fact)     # threshold crossings (=edges of checkerboard) for width (x-axis)
        lci_height, uci_height = threshold_crossings(diff_height, threshold_factor=thresh_fact)  # ..for height (y-axis)
        
        print('lower crossings:', lci_width)
        print('upper crossings:', uci_width)

        print('width..')
        width_position, left_width_position, right_width_position = checkerboard_position(lci_width, uci_width)
        print('height..')
        height_position, left_height_position, right_height_position = checkerboard_position(lci_height, uci_height)  # check if working
        
        top_left = np.array([left_width_position, left_height_position])
        top_right = np.array([right_width_position, left_height_position])
        bottom_left = np.array([left_width_position, right_height_position])
        bottom_right = np.array([right_width_position, right_height_position])

        print(top_left, top_right, bottom_left, bottom_right)
        fig, ax = plt.subplots()
        ax.imshow(frame)
        # ax.autoscale(False)
        for p in top_left, top_right, bottom_left, bottom_right:
            ax.scatter(p[0], p[1])
        ax.set_xlim(bottom_left_marker[0], bottom_right_marker[0])
        ax.set_ylim(bottom_left_marker[1], top_left_marker[1])
        # plt.show()
        
        # locations of checkerboard position do not yet fit the ones of the frame yet (visually checked)

        # embed()
        # quit()
        # find which indices (=pixels) represent edges of checkerboard by the corresponding sequence of ups and downs
        # both for width and height
        # assign x and y positions for the checkerboard corners

        # pixel to meter factor for default position with checkerboard in center of tank underneath camera
        
        # plt.plot(diff_width)
        plt.plot(diff_width)
        # plt.axhline(np.min(diff_height) / thresh_fact)
        # plt.axhline(np.max(diff_height) / thresh_fact)
        for l in lci_width:
            plt.axvline(l, color='yellow')
        for u in uci_width:
            plt.axvline(u, color='green')
        # plt.plot(frame_width, label='height')
        # plt.plot(frame_width, label='width')
        plt.legend()
        plt.show()
        embed()
        quit()
        

if __name__ == "__main__":    
    file_name = "/home/efish/etrack/videos/2022.03.28_3.mp4"
    frame_number = 10
    dc = DistanceCalibration(file_name=file_name, frame_number=frame_number)
    
    # marker_positions = dc.mark_crop_positions()
    
    dc.detect_checkerboard(file_name, frame_number=frame_number, marker_positions=np.load('marker_positions.npy', allow_pickle=True))
    
    # print(sys.argv[0])
    # print (sys.argv[1])
    # vid1 = sys.argv[1]
    
    # embed()