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 import glob from IPython import embed from calibration_functions import * class DistanceCalibration(): def __init__(self, file_name, frame_number, x_0=154, y_0=1318, cam_dist=1.36, tank_width=1.35, tank_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) -> None: super().__init__() 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._tank_width = tank_width self._tank_height = tank_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._x_factor = tank_width / width_pixel # m/pix self._y_factor = tank_height / height_pixel # m/pix self.distance_factor_calculation self.mark_crop_positions # if needed include setter: @y_0.setter def y_0(self, value): self._y_0 = value @property def x_0(self): return self._x_0 @property def y_0(self): return self._y_0 @property def cam_dist(self): return self._cam_dist @property def width(self): return self._width @property def height(self): return self._height @property def width_pix(self): return self._width_pix @property def height_pix(self): return self._height_pix @property def cb_width(self): return self._cb_width @property def cb_height(self): return self._cb_height @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): # load frame 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 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 # care: y-axis is inverted, top values are low, bottom values are high cropped_frame, frame_width, frame_height, diff_width, diff_height, _, _ = crop_frame(frame, marker_positions) # crop frame to given marker positions bottom_left_x = 0 bottom_left_y = np.shape(cropped_frame)[0] bottom_right_x = np.shape(cropped_frame)[1] bottom_right_y = np.shape(cropped_frame)[0] top_left_x = 0 top_left_y = 0 top_right_x = np.shape(cropped_frame)[1] top_right_y = 0 cropped_marker_positions = [{'bottom left corner': (bottom_left_x, bottom_left_y), 'top left corner': (top_left_x, top_left_y), 'top right corner': (top_right_x, top_right_y), 'bottom right corner': (bottom_right_x, bottom_right_y)}] 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) # position of checkerboard in width print('width..') width_position, left_width_position, right_width_position = checkerboard_position(lci_width, uci_width) # position of checkerboard in height print('height..') height_position, left_height_position, right_height_position = checkerboard_position(lci_height, uci_height) # left height refers to top, right height to bottom if width_position == 'left' and height_position == 'left': checkerboard_position_tank = 'top left' elif width_position == 'left' and height_position == 'right': checkerboard_position_tank = 'bottom left' elif width_position == 'right' and height_position == 'right': checkerboard_position_tank = 'bottom right' elif width_position == 'right' and height_position == 'left': checkerboard_position_tank = 'top right' else: checkerboard_position_tank = 'middle' print(checkerboard_position_tank) # final corner positions of checkerboard checkerboard_marker_positions = [{'bottom left corner': (left_width_position, right_height_position), 'top left corner': (left_width_position, left_height_position), 'top right corner': (right_width_position, left_height_position), 'bottom right corner': (right_width_position, right_height_position)}] print(checkerboard_marker_positions) checkerboard_top_right, checkerboard_top_left, checkerboard_bottom_right, checkerboard_bottom_left = assign_checkerboard_positions(checkerboard_marker_positions) fig, ax = plt.subplots() ax.imshow(cropped_frame) for p in checkerboard_top_left, checkerboard_top_right, checkerboard_bottom_left, checkerboard_bottom_right: ax.scatter(p[0], p[1]) ax.scatter(bottom_left_x, bottom_left_y) ax.scatter(bottom_right_x, bottom_right_y) ax.scatter(top_left_x, top_left_y) ax.scatter(top_right_x, top_right_y) plt.show() return checkerboard_marker_positions, cropped_marker_positions, checkerboard_position_tank def distance_factor_calculation(self, checkerboard_marker_positions, marker_positions): checkerboard_top_right, checkerboard_top_left, checkerboard_bottom_right, checkerboard_bottom_left = assign_checkerboard_positions(checkerboard_marker_positions) checkerboard_width = 0.24 checkerboard_height = 0.18 checkerboard_width_pixel = checkerboard_top_right[0] - checkerboard_top_left[0] checkerboard_height_pixel = checkerboard_bottom_right[1] - checkerboard_top_right[1] x_factor = checkerboard_width / checkerboard_width_pixel y_factor = checkerboard_height / checkerboard_height_pixel bottom_left_x, bottom_left_y, bottom_right_x, bottom_right_y, top_left_x, top_left_y, top_right_x, top_right_y = assign_marker_positions(marker_positions) tank_width_pixel = np.mean([bottom_right_x - bottom_left_x, top_right_x - top_left_x]) tank_height_pixel = np.mean([bottom_left_y - top_left_y, bottom_right_y - top_right_y]) tank_width = tank_width_pixel * x_factor tank_height = tank_height_pixel * y_factor print(tank_width, tank_height) return x_factor, y_factor def distance_factor_interpolation(x_factors, y_factors): pass if __name__ == "__main__": all_x_factor = [] all_y_factor = [] all_checkerboard_position_tank = [] for file_name in glob.glob("/home/efish/etrack/videos/*"): # file_name = "/home/efish/etrack/videos/2022.03.28_4.mp4" frame_number = 10 dc = DistanceCalibration(file_name=file_name, frame_number=frame_number) dc.mark_crop_positions() checkerboard_marker_positions, cropped_marker_positions, checkerboard_position_tank = dc.detect_checkerboard(file_name, frame_number=frame_number, marker_positions=np.load('marker_positions.npy', allow_pickle=True)) x_factor, y_factor = dc.distance_factor_calculation(checkerboard_marker_positions, marker_positions=cropped_marker_positions) all_x_factor.append(x_factor) all_y_factor.append(y_factor) all_checkerboard_position_tank.append(checkerboard_position_tank) x_factors = np.load('x_factors.npy') y_factors = np.load('y_factors.npy') all_checkerboard_position_tank = np.load('all_checkerboard_position_tank.npy') embed() quit() # next up: distance calculation with angle # is this needed or are current videos enough?: # laying checkerboard at position directly above and below / left and right to centered checkerboard near edge of tank # calculating x and y factor for centered checkerboard, then for the ones at the edge # --> afterwards interpolate between them to have continuous factors for whole tank # maybe smaller object in tank to have more accurate factor # make function to refine checkerboard detection at edges of tank by saying if no lower color values appears near edge --> checkerboard position then == corner of tank? # # mark_crop_positions why failing plot at end? # with rectangles of checkerboard? # embed()