def test_foreground_probability(savegif=False):
subplt_titles = ["Original", "Foreground %"]
ims = init_comparison_plot(get_frame_GREY(data[0]),
subplt_titles,
1,
2,
title="Probabilistic Foreground Detection")
for i in range(len(data)):
frame = get_frame(data[i])
L_frame = L[:, i].reshape(width, height).T
S_frame = S[:, i].reshape(width, height).T
L_probability = foreground_probability(L_frame, frame)
# this condition is now abstracted as the function cleaned_godec_img() in background_subtractio.py
S_probability = foreground_probability(S_frame, frame)
if np.amax(L_probability) > np.amax(S_probability):
probability = L_probability
img = L_frame
else:
probability = S_probability
img = S_frame
images = [normalize_frame(img), normalize_frame(probability)]
update_comparison_plot(ims, images)
create_folder_if_absent("testpics")
plt.savefig("testpics/{}.png".format(i))
if savegif:
files = get_all_files("testpics")
write_gif_from_pics(files,
"probabilistic_foreground_detection.gif",
fps=20)
def test_analyze_centroid_displacement_history():
data_path = "data/teck_one_day_activity"
files = get_all_files(data_path)
analysis_results = analyze_centroid_displacement_history(files)
print(analysis_results)
write_to_json(analysis_results,
"displacement_{}.json".format(basename(data_path)))
def test_analyze_centroid_area_history_short_time():
data_path = "data/teck_calib_2"
files = get_all_files(data_path)
analysis_results = analyze_centroid_area_history(files)
print(analysis_results)
write_to_json(
analysis_results, "sample_presence_detection_analysis/{}.json".format(
basename(data_path)))
def test_naive_presence_detection_pipeline():
start = time.time()
folder_name = "sw_first_trial"
data_path = "./data/" + folder_name
files = get_all_files(data_path)
print("Number of frames: ", len(files))
result = analyze()
end = time.time()
print("Analysis completed in ", end - start, "seconds")
write_to_json(result,
"./sample_time_series_results/{}.json".format(folder_name))
def test_analyze_centroid_displacement_history():
start = time.time()
data_path = "data/dataset_for_xavier/2020.07.16"
files = get_all_files(data_path)
print("Number of files: {}".format(len(files)))
analysis_results = analyze_centroid_displacement_history(files)
json_name = basename(data_path)
print("Analysis done, writing to {}.json...".format(json_name))
write_to_json(analysis_results,
"displacement_history/{}.json".format(json_name))
end = time.time()
print("Time taken to collect displacement dictionary for {} files : {}".
format(len(files), end - start))
def naive_detection_from_files(data_path, startIndex=None, endIndex=None):
heatmap_plot = get_init_heatmap_plot()
likelihood_plot = get_init_likelihood_plot()
files = get_all_files(data_path)
if startIndex == None:
startIndex = 0
if endIndex == None:
endIndex = len(files)
print(startIndex, endIndex)
for i in range(startIndex, endIndex):
frame = get_frame(files[i])
areas_person_is_in = naive_detection_by_frame(frame)
likelihood_array = [[
areas_person_is_in[i]["likelihood"] for i in range(4)
], [areas_person_is_in[i]["likelihood"] for i in range(4, 8)]]
# if debugging with plot view
update_heatmap(frame, heatmap_plot)
update_heatmap(likelihood_array, likelihood_plot)
from presence_detection import get_centroid_area_number, get_centroid_area_history
from background_subtraction_test import test_postprocess_img
from centroid_history import plot_centroid_history
from file_utils import get_all_files
from naive_presence_detection import (get_frame,
naive_binary_likelihood_by_frame,
naive_detection_by_frame,
naive_detection_from_files,
optical_flow_dense, optical_flow_lk,
visualize_likelihood_plot)
data_path = "./data/teck_walk_out_and_in" # dirty data with movement
# data_path = "./data/sw_second_trial" # very dirty data with no movement
files = get_all_files(data_path)
print("Number of frames found in ", data_path, ": ", len(files))
"""
Naive Presence Detection Tests
"""
def test_naive_one_frame():
# view normal heatmap next to percentage plot
test_frame = get_frame(files[60 * 20])
areas_person_is_in = naive_detection_by_frame(test_frame)
fig, ax = plt.subplots()
im = ax.imshow(test_frame, cmap="hot")
visualize_likelihood_plot(areas_person_is_in)
def test_naive_many_frames():
naive_detection_from_files(files, 7200, 12000)
import matplotlib.pyplot as plt
import numpy as np
from background_subtraction_test import test_postprocess_img
from centroid_history import (Interpolator, get_centroid_area_history,
get_centroid_area_number,
displacement_history,
get_centroid_history, input_target_centroid_area,
plot_centroid_history_hexbin)
from file_utils import get_all_files
from visualizer import init_heatmap, update_heatmap
data = "data/teck_calib_2"
files = get_all_files(data)
def test_input_target_centroid_area():
input_target_centroid_area()
def test_get_centroid_area_number():
centroid = (0,0)
print("Area number for centroid ", centroid, " is ", get_centroid_area_number(centroid))
centroid = (12,0)
print("Area number for centroid ", centroid, " is ", get_centroid_area_number(centroid))
centroid = (0,12)
print("Area number for centroid ", centroid, " is ", get_centroid_area_number(centroid))
centroid = (6,24)
print("Area number for centroid ", centroid, " is ", get_centroid_area_number(centroid))
def test_get_centroid_area_number_from_postprocess(file):
thresholded_img, centroids = test_postprocess_img(file, plot=True)
centroid = centroids[0]
from file_utils import get_all_files
from optical_flow import optical_flow_dense, optical_flow_lk
from visualizer import write_gif_from_pics
def test_opticalflow_lk():
print("Performing Lucas-Kanade Optical Flow")
optical_flow_lk(files)
def test_opticalflow_dense():
print("Performing Dense Optical Flow with Gunnar Farneback")
optical_flow_dense(files)
data_path = "data/teck_walk_out_and_in"
files = get_all_files(data_path)
test_opticalflow_dense()
pics = get_all_files("optical_flow_pics/")
write_gif_from_pics(pics, "dense_v2.gif", start=0, end=len(pics), fps=5)
def test_bs_godec(files,
save_data=False,
save_gif=False,
gif_name=None,
fps=60):
"""Test bs_godec Implementation
Keyword Arguments:
save_data {bool} -- (default: {False})
save_gif {bool} -- (default: {False})
gif_name {string} -- (default: {None})
fps {int} -- (default: {60})
---
This test shows you different ways of running the bs_godec method and obtain the various type of data you need.
"""
print("Testing Godec...")
t = Timer("accumulate")
t.start()
M, LS, L, S, width, height = bs_godec(files)
print("M: ")
print(M)
print("L: ")
print(L)
t.stop("Time taken to run background subtraction with godec: ")
if save_data:
t.start()
npy_path = "godec_data/" + basename(data_path)
create_folder_if_absent(godec_data_path)
save_npy(M, npy_path, "M")
save_npy(L, npy_path, "L")
save_npy(S, npy_path, "S")
save_npy(LS, npy_path, "LS")
t.stop("Time taken to save godec result npy arrays: ")
plots_path = godec_pics_path + basename(data_path)
if not save_gif:
print("Plotting....")
t.start()
plot_godec(M,
LS,
L,
S,
plots_path,
width=width,
height=height,
preview=True)
t.stop("Time taken to save godec plots: ")
elif save_gif and gif_name:
print("Saving gif as {}....".format(gif_name))
t.start()
plot_godec(M, LS, L, S, plots_path, width=width, height=height)
pics = get_all_files(plots_path)
t.stop("Time taken to save godec plots: ")
write_gif(pics,
godec_gifs_path + gif_name,
start=0,
end=len(pics),
fps=fps)
print("The entire process has taken: ", t.timers['accumulate'], " seconds")
# backgrounds = []
#
# for i in range(len(data)):
#
# if i == 2:
# background = generate_background_est(m, background, first_uncluttered, 0.2)
# elif i > 2:
# background = generate_background_est(m, backgrounds[i - 2], result[i - 1], 0.2)
#
# backgrounds.append(background)
# r = get_frame_GREY(data[i]) - background
# print(r + background == get_frame_GREY(data[i]))
# result.append(r)
# @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@__xxXX__TEST____XXxx@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
data = get_all_files("data/teck_walk_out_and_in")
result, backgrounds = static_clutter_algo(data)
plt.subplot(131)
plt.imshow(get_frame_GREY(data[29]))
# plt.title("Original Image %d" % i)
plt.subplot(132)
plt.imshow(result[29])
# plt.title("clutter removed %d" % i)
plt.subplot(133)
plt.imshow(backgrounds[29])
# plt.title("backgrounds %d" % i)
plt.show()
