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Fix registration #18

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Merged
dubusster merged 3 commits into from
Oct 4, 2023
Merged

Fix registration #18

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b715ba4
clean imports
dubusster Oct 3, 2023
4451f4c
fix: :bug: fix attribute name in exception
dubusster Oct 3, 2023
d419e80
style: :art: some import cleaning + typing
dubusster Oct 3, 2023
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11 changes: 5 additions & 6 deletions src/python/librir/registration/compute_registration.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
###########################

import os
from librir.video_io import IRMovie
import numpy as np
import pandas as pd
import cv2
Expand All @@ -17,7 +18,7 @@
###########################


def manage_computation_and_tries(img, regis_obj):
def manage_computation_and_tries(img, regis_obj: MaskedRegistratorECC):
"""
Fonction qui calcule les deplacements en x, en y et le niveau de
confiance. Si l'algorithme ne parvient pas a converger pour une image,
Expand All @@ -36,7 +37,7 @@ def manage_computation_and_tries(img, regis_obj):
if regis_obj.check_median_value(1):
regis_obj.define_median_value(1)
except cv2.error:
regis_obj.decrease_median_value(0.01)
regis_obj.decrease_median(0.01)
nb_try += 1
if nb_try > 0:
print("try number : {}".format(nb_try))
Expand All @@ -49,7 +50,7 @@ def manage_computation_and_tries(img, regis_obj):


def compute_confidence_and_x_y_trajectories(
pulse_obj, view, lower_bound, upper_bound, calibration, *args
ir_movie: IRMovie, view, lower_bound, upper_bound, calibration, *args
):
"""
Fonction qui permet d'estimer les deplacements (x,y) des pixels
Expand All @@ -61,10 +62,9 @@ def compute_confidence_and_x_y_trajectories(
is_div = False

for img_number in range(lower_bound, upper_bound + 1):

if img_number % 50 == 0:
print(img_number)
img = pulse_obj.load_pos(img_number, calibration)
img = ir_movie.load_pos(img_number, calibration)

if is_div:
regis_obj_up = manage_computation_and_tries(img, args[0])
Expand Down Expand Up @@ -310,7 +310,6 @@ def compute_registration_ir(view_name, pulse_or_filename, outfile):


if __name__ == "__main__":

# Arguments: view_name pulse_or_file out_file
import sys

Expand Down
22 changes: 11 additions & 11 deletions src/python/librir/registration/masked_registration_ecc.py
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Original file line number Diff line number Diff line change
Expand Up @@ -5,9 +5,12 @@
@author: CS266247
"""

from librir.signal_processing.rir_signal_processing import (
find_median_pixel,
gaussian_filter,
translate,
)
import numpy as np
import librir as lr
import librir.signal_processing
import cv2

########################
Expand Down Expand Up @@ -62,7 +65,7 @@ def __init__(
if ref is not None and pre_process is not None:
self.ref = pre_process(ref)
if sigma > 0 and self.ref is not None:
self.ref = lr.signal_processing.gaussian_filter(self.ref, sigma)
self.ref = gaussian_filter(self.ref, sigma)
self.mask_ref_img = None
self.window_factorH = window_factorh
self.window_factorV = window_factorv
Expand All @@ -82,7 +85,7 @@ def start(self, img):
if self.pre_process is not None:
img = self.pre_process(img)
if self.sigma > 0:
img = lr.signal_processing.gaussian_filter(img, self.sigma)
img = gaussian_filter(img, self.sigma)

self.ref_img = img[
self.startY : self.startY + self.subH, self.startX : self.startX + self.subW
Expand All @@ -102,7 +105,7 @@ def compute(self, img):
if self.pre_process is not None:
img = self.pre_process(img)
if self.sigma > 0:
img = lr.signal_processing.gaussian_filter(img, self.sigma)
img = gaussian_filter(img, self.sigma)
new_im = img[
self.startY : self.startY + self.subH, self.startX : self.startX + self.subW
].copy()
Expand Down Expand Up @@ -140,11 +143,8 @@ def compute(self, img):
mask = self.mask

if self.median < 1:

thresh1 = lr.signal_processing.find_median_pixel(new_im, self.median, mask)
thresh2 = lr.signal_processing.find_median_pixel(
self.ref_img, self.median, mask
)
thresh1 = find_median_pixel(new_im, self.median, mask)
thresh2 = find_median_pixel(self.ref_img, self.median, mask)
thresh = max(thresh1, thresh2)

m = (im1 > thresh) | (im2 > thresh)
Expand Down Expand Up @@ -183,7 +183,7 @@ def compute(self, img):
print("confidence:", self.conf_thresh)
if confidence < self.conf_thresh:
print("reset at confidence ", len(self.x), confidence)
new_im = lr.signal_processing.translate(new_im, -shift[1], -shift[0])
new_im = translate(new_im, -shift[1], -shift[0])
self.ref_img = new_im
self.start_mat = np.eye(2, 3, dtype=np.float32)

Expand Down