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[distance_calibration] sequence checkerboard detection

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Xaver Roos 2022-04-01 16:14:28 +02:00
parent 8199fe4c7f
commit 0488ca6e64
3 changed files with 201 additions and 77 deletions
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1
etrack/__init__.py
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@ -1,2 +1,3 @@
from .image_marker import ImageMarker, MarkerTask
from .tracking_result import TrackingResult
from .distance_calibration import DistanceCalibration

259
etrack/distance_calibration.py
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@ -1,17 +1,21 @@
from cv2 import calibrationMatrixValues
from multiprocessing import allow_connection_pickling
from turtle import left
from cv2 import MARKER_TRIANGLE_UP, calibrationMatrixValues, 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
class DistanceCalibration:
class DistanceCalibration():
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,
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.
@ -21,9 +25,9 @@ class DistanceCalibration:
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.
heigth (int, optional): Height 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): Heigth in pixel from one lightened corner of the tank to the other. Defaults to 1375.
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.
@ -47,15 +51,25 @@ class DistanceCalibration:
self._x_factor = self.width / self.width_pix # m/pix
self._y_factor = self.height / self.height_pix # m/pix
# properties
self.mark_crop_positions
self.threshold_crossings
@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
@ -64,18 +78,50 @@ class DistanceCalibration:
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
@ -85,43 +131,86 @@ class DistanceCalibration:
return self._y_factor
def crop_movie():
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
if success:
self._fig.gca().imshow(frame) # plot wanted frame of video
else:
print("Could not read frame number %i either failed to open movie or beyond maximum frame number!" % frame_number)
return []
plt.ion() # turn on interactive mode
plt.show(block=False) # block=False allows to continue interact in terminal while the figure is open
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
self._task_index = -1
if len(self._tasks) > 0:
self._next_task()
def crop_frame(self, frame, marker_positions):
while not self._tasks_done:
plt.pause(0.250)
if self._interrupt:
return []
bottom_left = marker_positions[0]['bottom left corner']
bottom_right = marker_positions[0]['bottom right corner']
top_left = marker_positions[0]['top left corner']
top_right = marker_positions[0]['top right corner']
self._fig.gca().set_title("All set and done!\n Window will close in 2s")
self._fig.canvas.draw()
plt.pause(2.0)
plt.close()
return [t.marker_positions for t in self._tasks]
left_bound = int(np.mean([bottom_left[0], top_left[0]]))
right_bound = int(np.mean([bottom_right[0], top_right[0]]))
top_bound = int(np.mean([top_left[1], top_right[1]]))
bottom_bound = int(np.mean([bottom_left[1], bottom_right[1]]))
crop_frame = frame[top_bound:bottom_bound, left_bound:right_bound]
crop_frame = np.mean(crop_frame, axis=2)
frame_width = np.mean(crop_frame,axis=0)
frame_height = np.mean(crop_frame,axis=1)
diff_width = np.diff(frame_width)
diff_height = np.diff(frame_height)
x_width = np.arange(0, len(diff_width), 1)
x_height = np.arange(0, len(diff_height), 1)
return frame_width, frame_height, diff_width, diff_height, x_width, x_height
def rotation_angle():
pass
def threshold_crossings(self, data, threshold_factor):
lower_threshold = np.min(data) / threshold_factor
upper_threshold = np.max(data) / threshold_factor
lower_crossings = np.diff(data < lower_threshold, prepend=False)
upper_crossings = np.diff(data > upper_threshold, append=False)
lower_crossings_indices = np.argwhere(lower_crossings)
upper_crossings_indices = np.argwhere(upper_crossings)
half_window_size = 10
lower_peaks = []
upper_peaks = []
for lower_idx in lower_crossings_indices:
if lower_idx < half_window_size:
half_window_size = lower_idx
window = data[lower_idx[0] - int(half_window_size):lower_idx[0] + int(half_window_size)]
min_window = np.min(window)
min_idx = np.where(data == min_window)
lower_peaks.append(min_idx)
for upper_idx in upper_crossings_indices:
if upper_idx < half_window_size:
half_window_size = upper_idx
window = data[upper_idx[0] - int(half_window_size) : upper_idx[0] + int(half_window_size)]
max_window = np.max(window)
max_idx = np.where(data == max_window)
upper_peaks.append(max_idx)
lower_peaks = np.unique(lower_peaks)
upper_peaks = np.unique(upper_peaks)
return lower_peaks, upper_peaks
def detect_checkerboard(self, filename, frame_number, marker_positions):
def mark_checkerboard(self, filename, frame_number=10):
if not os.path.exists(filename):
raise IOError("file %s does not exist!" % filename)
video = cv2.VideoCapture()
@ -139,47 +228,81 @@ class DistanceCalibration:
print("Reading frame: %i" % frame_counter, end="\r")
success, frame = video.read()
frame_counter += 1
width_mean = np.mean(frame,axis=1)
crop_width_mean = width_mean[x_0:width_pix]
height_mean = np.mean(frame,axis=0)
crop_height_mean = height_mean[y_0:height_pix]
# HELLO, here you at
marker_positions = np.load('marker_positions.npy', allow_pickle=True)
frame_width, frame_height, diff_width, diff_height, _, _ = dc.crop_frame(frame, marker_positions)
# y-axis is inverted..
thresh_fact = 7
lci_width, uci_width = dc.threshold_crossings(diff_width, threshold_factor=thresh_fact)
lci_height, uci_height = dc.threshold_crossings(diff_height, threshold_factor=thresh_fact)
print('lower crossings:', lci_width)
print('upper crossings:', uci_width)
# make function for this
zip_list = []
for zl in lci_width:
zip_list.append(zl)
for zu in uci_width:
zip_list.append(zu)
zip_list = np.sort(zip_list)
sequence = []
for z in zip_list:
if z in lci_width:
sequence.append('down')
else:
sequence.append('up')
print('sequence:', sequence)
if sequence == ['up', 'down', 'up', 'down']:
print('in middle')
# first down, second up are edges of checkerboard
elif sequence == ['up', 'up', 'down']:
print('at left')
# first and second up are edges of checkerboard
else:
print('at right')
# first and second down are edges of checkerboard
# find mistake in threshold detection (_7.mp4) where two detections at side (by thresh factor)
# 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.axhline(np.min(diff_width) / thresh_fact)
plt.axhline(np.max(diff_width) / 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_height)
plt.plot(frame_width)
plt.show()
embed()
quit()
if success:
self._fig.gca().imshow(frame) # plot wanted frame of video
else:
print("Could not read frame number %i either failed to open movie or beyond maximum frame number!" % frame_number)
return []
plt.ion() # turn on interactive mode
plt.show(block=False) # block=False allows to continue interact in terminal while the figure is open
self._task_index = -1
if len(self._tasks) > 0:
self._next_task()
# rotation angle
while not self._tasks_done:
plt.pause(0.250)
if self._interrupt:
return []
self._fig.gca().set_title("All set and done!\n Window will close in 2s")
self._fig.canvas.draw()
plt.pause(2.0)
plt.close()
return [t.marker_positions for t in self._tasks]
if __name__ == "__main__":
file_name = "/home/efish/etrack/videos/2022.03.28_7.mp4"
frame_number = 10
dc = DistanceCalibration(file_name=file_name, frame_number=frame_number)
# marker_positions = dc.mark_crop_positions()
if __name__ == "__main__":
vid2 = "/home/efish/etrack/videos/2022.03.28_3.mp4"
calibration_task = DistanceCalibration(vid2)
dc = DistanceCalibration(calibration_task)
dc.mark_checkerboard(vid2, 10)
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()
# embed()

4
etrack/image_marker.py
View File

@ -20,7 +20,7 @@ class ImageMarker:
self._fig.canvas.mpl_connect('close_event', self._fig_close_event)
self._fig.canvas.mpl_connect('key_press_event', self._key_press_event)
def mark_movie(self, filename, frame_number=0):
def mark_movie(self, filename, frame_number=10):
""" Interactive GUI to mark the corners of the tank. A specific frame of the video can be chosen. Returns marker positions.
Args:
@ -161,7 +161,7 @@ if __name__ == "__main__":
im = ImageMarker(tasks)
vid1 = "/home/efish/efish_tracking/efish_tracking3-Xaver-2022-03-21/videos/2022.01.12_3DLC_resnet50_efish_tracking3Mar21shuffle1_300000_labeled.mp4"
marker_positions = im.mark_movie(vid1, 100)
marker_positions = im.mark_movie(vid1, 10)
print(marker_positions)
# print(sys.argv[0])