forked from jgrewe/efish_tracking
[distance_calibration] sequence checkerboard detection
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0488ca6e64
@ -1,2 +1,3 @@
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from .image_marker import ImageMarker, MarkerTask
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from .tracking_result import TrackingResult
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from .tracking_result import TrackingResult
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from .distance_calibration import DistanceCalibration
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@ -1,17 +1,21 @@
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from cv2 import calibrationMatrixValues
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from multiprocessing import allow_connection_pickling
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from turtle import left
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from cv2 import MARKER_TRIANGLE_UP, calibrationMatrixValues, threshold
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import matplotlib.pyplot as plt
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import numpy as np
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import cv2
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import os
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import sys
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from IPython import embed
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from etrack import MarkerTask, ImageMarker
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class DistanceCalibration:
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class DistanceCalibration():
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def __init__(self, file_name, x_0=95, y_0=185, cam_dist=1.36, width=1.35, height=0.805, width_pixel=1975, height_pixel=1375, checkerboard_width=0.24, checkerboard_height=0.18,
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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,
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checkerboard_width_pixel=500, checkerboard_height_pixel=350, rectangle_width=0.024, rectangle_height=0.0225, rectangle_width_pixel=100, rectangle_height_pixel=90,
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rectangle_count_width=9, rectangle_count_height=7) -> None:
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rectangle_count_width=9, rectangle_count_height=7) -> None:
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super().__init__()
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# aktualisieren
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"""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.
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@ -21,9 +25,9 @@ class DistanceCalibration:
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y_0 (int, optional): Y-value of the "origin" of the tank. Defaults to 0.
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cam_dist (int, optional): Distance of camera lense to tank floor. Defaults to 1.36.
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width (int, optional): Width in meter from one lightened corner of the tank to the other. Defaults to 1.35.
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heigth (int, optional): Height in meter from one lightened corner of the tank to the other. Defaults to 1.35.
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height (int, optional): Height in meter from one lightened corner of the tank to the other. Defaults to 1.35.
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width_pixel (int, optional): Width in pixel from one lightened corner of the tank to the other. Defaults to 1975.
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height_pixel (int, optional): Heigth in pixel from one lightened corner of the tank to the other. Defaults to 1375.
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height_pixel (int, optional): Height in pixel from one lightened corner of the tank to the other. Defaults to 1375.
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rectangle_width (float, optional): Width of one black or corresponding white rectangle of the checkerboard. Defaults to 0.024.
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rectangle_height (float, optional): Height of one black or corresponding white rectangle of the checkerboard. Defaults to 0.0225.
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rectangle_count_width (int, optional): Number of black rectangles over the width of the whole checkerboard. Defaults to 9.
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@ -47,15 +51,25 @@ class DistanceCalibration:
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self._x_factor = self.width / self.width_pix # m/pix
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self._y_factor = self.height / self.height_pix # m/pix
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# properties
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self.mark_crop_positions
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self.threshold_crossings
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@property
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def x_0(self):
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return self._x_0
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@x_0.setter
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def x_0(self, value):
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self._x_0 = value
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@property
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def y_0(self):
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return self._y_0
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@y_0.setter
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def y_0(self, value):
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self._y_0 = value
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@property
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def cam_dist(self):
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return self._cam_dist
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@ -63,65 +77,140 @@ class DistanceCalibration:
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@property
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def width(self):
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return self._width
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@width.setter
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def width(self, value):
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self._width = value
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@property
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def height(self):
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return self._height
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@height.setter
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def height(self, value):
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self._height = value
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@property
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def width_pix(self):
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return self._width_pix
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@width_pix.setter
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def width_pix(self, value):
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self._width_pix = value
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@property
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def height_pix(self):
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return self._height_pix
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@height_pix.setter
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def height_pix(self, value):
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self._height_pix_ = value
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@property
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def cb_width(self):
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return self._cb_width
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@cb_width.setter
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def cb_width(self, value):
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self._cb_width = value
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@property
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def cb_height(self):
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return self._cb_height
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@cb_height.setter
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def cb_height(self, value):
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self._cb_height = value
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@property
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def x_factor(self):
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return self._x_factor
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@property
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def y_factor(self):
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return self._y_factor
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return self._y_factor
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def crop_movie():
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if not os.path.exists(filename):
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raise IOError("file %s does not exist!" % filename)
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video = cv2.VideoCapture()
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video.open(filename)
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frame_counter = 0
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success = True
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frame = None
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while success and frame_counter <= frame_number: # iterating until frame_counter == frame_number --> success (True)
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print("Reading frame: %i" % frame_counter, end="\r")
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success, frame = video.read()
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frame_counter += 1
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if success:
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self._fig.gca().imshow(frame) # plot wanted frame of video
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else:
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print("Could not read frame number %i either failed to open movie or beyond maximum frame number!" % frame_number)
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return []
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plt.ion() # turn on interactive mode
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plt.show(block=False) # block=False allows to continue interact in terminal while the figure is open
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def mark_crop_positions(self):
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task = MarkerTask("crop area", ["bottom left corner", "top left corner", "top right corner", "bottom right corner"], "Mark crop area")
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im = ImageMarker([task])
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marker_positions = im.mark_movie(file_name, frame_number)
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print(marker_positions)
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np.save('marker_positions', marker_positions)
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return marker_positions
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def crop_frame(self, frame, marker_positions):
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bottom_left = marker_positions[0]['bottom left corner']
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bottom_right = marker_positions[0]['bottom right corner']
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top_left = marker_positions[0]['top left corner']
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top_right = marker_positions[0]['top right corner']
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left_bound = int(np.mean([bottom_left[0], top_left[0]]))
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right_bound = int(np.mean([bottom_right[0], top_right[0]]))
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top_bound = int(np.mean([top_left[1], top_right[1]]))
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bottom_bound = int(np.mean([bottom_left[1], bottom_right[1]]))
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crop_frame = frame[top_bound:bottom_bound, left_bound:right_bound]
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crop_frame = np.mean(crop_frame, axis=2)
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frame_width = np.mean(crop_frame,axis=0)
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frame_height = np.mean(crop_frame,axis=1)
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diff_width = np.diff(frame_width)
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diff_height = np.diff(frame_height)
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x_width = np.arange(0, len(diff_width), 1)
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x_height = np.arange(0, len(diff_height), 1)
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return frame_width, frame_height, diff_width, diff_height, x_width, x_height
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def rotation_angle():
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pass
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def threshold_crossings(self, data, threshold_factor):
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lower_threshold = np.min(data) / threshold_factor
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upper_threshold = np.max(data) / threshold_factor
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self._task_index = -1
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if len(self._tasks) > 0:
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self._next_task()
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lower_crossings = np.diff(data < lower_threshold, prepend=False)
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upper_crossings = np.diff(data > upper_threshold, append=False)
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while not self._tasks_done:
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plt.pause(0.250)
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if self._interrupt:
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return []
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lower_crossings_indices = np.argwhere(lower_crossings)
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upper_crossings_indices = np.argwhere(upper_crossings)
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half_window_size = 10
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lower_peaks = []
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upper_peaks = []
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for lower_idx in lower_crossings_indices:
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if lower_idx < half_window_size:
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half_window_size = lower_idx
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window = data[lower_idx[0] - int(half_window_size):lower_idx[0] + int(half_window_size)]
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min_window = np.min(window)
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min_idx = np.where(data == min_window)
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lower_peaks.append(min_idx)
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self._fig.gca().set_title("All set and done!\n Window will close in 2s")
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self._fig.canvas.draw()
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plt.pause(2.0)
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plt.close()
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return [t.marker_positions for t in self._tasks]
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for upper_idx in upper_crossings_indices:
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if upper_idx < half_window_size:
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half_window_size = upper_idx
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window = data[upper_idx[0] - int(half_window_size) : upper_idx[0] + int(half_window_size)]
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max_window = np.max(window)
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max_idx = np.where(data == max_window)
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upper_peaks.append(max_idx)
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lower_peaks = np.unique(lower_peaks)
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upper_peaks = np.unique(upper_peaks)
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return lower_peaks, upper_peaks
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def mark_checkerboard(self, filename, frame_number=10):
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def detect_checkerboard(self, filename, frame_number, marker_positions):
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if not os.path.exists(filename):
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raise IOError("file %s does not exist!" % filename)
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video = cv2.VideoCapture()
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@ -139,47 +228,81 @@ class DistanceCalibration:
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print("Reading frame: %i" % frame_counter, end="\r")
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success, frame = video.read()
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frame_counter += 1
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width_mean = np.mean(frame,axis=1)
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crop_width_mean = width_mean[x_0:width_pix]
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height_mean = np.mean(frame,axis=0)
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crop_height_mean = height_mean[y_0:height_pix]
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# HELLO, here you at
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embed()
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quit()
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if success:
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self._fig.gca().imshow(frame) # plot wanted frame of video
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else:
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print("Could not read frame number %i either failed to open movie or beyond maximum frame number!" % frame_number)
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return []
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plt.ion() # turn on interactive mode
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plt.show(block=False) # block=False allows to continue interact in terminal while the figure is open
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marker_positions = np.load('marker_positions.npy', allow_pickle=True)
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frame_width, frame_height, diff_width, diff_height, _, _ = dc.crop_frame(frame, marker_positions)
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self._task_index = -1
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if len(self._tasks) > 0:
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self._next_task()
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# y-axis is inverted..
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while not self._tasks_done:
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plt.pause(0.250)
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if self._interrupt:
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return []
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thresh_fact = 7
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lci_width, uci_width = dc.threshold_crossings(diff_width, threshold_factor=thresh_fact)
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lci_height, uci_height = dc.threshold_crossings(diff_height, threshold_factor=thresh_fact)
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print('lower crossings:', lci_width)
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print('upper crossings:', uci_width)
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self._fig.gca().set_title("All set and done!\n Window will close in 2s")
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self._fig.canvas.draw()
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plt.pause(2.0)
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plt.close()
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return [t.marker_positions for t in self._tasks]
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# make function for this
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zip_list = []
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for zl in lci_width:
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zip_list.append(zl)
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for zu in uci_width:
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zip_list.append(zu)
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zip_list = np.sort(zip_list)
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if __name__ == "__main__":
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vid2 = "/home/efish/etrack/videos/2022.03.28_3.mp4"
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calibration_task = DistanceCalibration(vid2)
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dc = DistanceCalibration(calibration_task)
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dc.mark_checkerboard(vid2, 10)
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sequence = []
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for z in zip_list:
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if z in lci_width:
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sequence.append('down')
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else:
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sequence.append('up')
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print('sequence:', sequence)
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if sequence == ['up', 'down', 'up', 'down']:
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print('in middle')
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# first down, second up are edges of checkerboard
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elif sequence == ['up', 'up', 'down']:
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print('at left')
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# first and second up are edges of checkerboard
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else:
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print('at right')
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# first and second down are edges of checkerboard
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# find mistake in threshold detection (_7.mp4) where two detections at side (by thresh factor)
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# find which indices (=pixels) represent edges of checkerboard by the corresponding sequence of ups and downs
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# both for width and height
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# assign x and y positions for the checkerboard corners
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# pixel to meter factor for default position with checkerboard in center of tank underneath camera
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plt.plot(diff_width)
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plt.axhline(np.min(diff_width) / thresh_fact)
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plt.axhline(np.max(diff_width) / thresh_fact)
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for l in lci_width:
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plt.axvline(l, color='yellow')
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for u in uci_width:
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plt.axvline(u, color='green')
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# plt.plot(frame_height)
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plt.plot(frame_width)
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plt.show()
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embed()
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quit()
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# rotation angle
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if __name__ == "__main__":
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file_name = "/home/efish/etrack/videos/2022.03.28_7.mp4"
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frame_number = 10
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dc = DistanceCalibration(file_name=file_name, frame_number=frame_number)
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# marker_positions = dc.mark_crop_positions()
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dc.detect_checkerboard(file_name, frame_number=frame_number, marker_positions=np.load('marker_positions.npy', allow_pickle=True))
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# print(sys.argv[0])
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# print (sys.argv[1])
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# vid1 = sys.argv[1]
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embed()
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# embed()
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@ -20,7 +20,7 @@ class ImageMarker:
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self._fig.canvas.mpl_connect('close_event', self._fig_close_event)
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self._fig.canvas.mpl_connect('key_press_event', self._key_press_event)
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def mark_movie(self, filename, frame_number=0):
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def mark_movie(self, filename, frame_number=10):
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""" Interactive GUI to mark the corners of the tank. A specific frame of the video can be chosen. Returns marker positions.
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Args:
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@ -161,7 +161,7 @@ if __name__ == "__main__":
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im = ImageMarker(tasks)
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vid1 = "/home/efish/efish_tracking/efish_tracking3-Xaver-2022-03-21/videos/2022.01.12_3DLC_resnet50_efish_tracking3Mar21shuffle1_300000_labeled.mp4"
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marker_positions = im.mark_movie(vid1, 100)
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marker_positions = im.mark_movie(vid1, 10)
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print(marker_positions)
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# print(sys.argv[0])
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