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import_results_neo.py
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import_results_neo.py
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import os
import re
import cv2
import numpy as np
import decimal
import matplotlib.pyplot as plt
from add_bbox import *
# Import the results from the results.txt file
def import_and_filter_result_neo(input_file='/home/as-hunt/result.txt', results_file='results.txt', obj_names='/home/as-hunt/Etra-Space/white-thirds/obj.names'):
'''Import's Yolo darknet detection results bouding boxes.
This function does filters the result.txt file.
It removes bouding boxes that are outside the image and
bouding boxes that are too close to the edge of the image.
Args:
input_file (str): The path to the results.txt file
results_file (str): The path to the file to save the filtered results
obj_names (str): The path to the obj.names file
'''
arry = []
res = open(results_file, 'w')
with open(obj_names, 'r') as f:
for line in f:
arry.append(line.rstrip())
with open(input_file, 'r') as f:
for line in f:
if line[0:4] == '/hom':
lin = re.split('/| ', line)
li = filter(lambda a: '.jpg' in a, lin)
l = list(li)[0][:-5]
image_name = l
elif (line[0:3] in arry) or (line[0:4] in arry ) == True:
lin = re.split(':|%|t|w|h', line)
if int(lin[4]) < 4:
pass
elif int(lin[4]) > 412:
pass
else:
if int(lin[6]) < 4:
pass
elif int(lin[6]) > 412:
pass
else:
classes = int(arry.index(lin[0]))
confidence = int((lin[1]))
if int(lin[4]) < 0:
left_x = 0
else:
left_x = int(lin[4])
if int(lin[6]) < 0:
top_y = 0
else:
top_y = int(lin[6])
width = int(lin[10])
height = int(lin[14][:-2])
bottom_y = top_y + height
right_x = left_x + width
if bottom_y < 0:
bottom_y = 0
if right_x > 416:
right_x = 416
if bottom_y < 4:
pass
elif bottom_y > 412:
pass
else:
if right_x > 412:
pass
elif right_x < 4:
pass
else:
res.write(image_name + ' ' + str(classes) + ' ' + str(left_x) + ' ' + str(top_y) + ' ' + str(right_x) + ' ' + str(bottom_y) + ' ' + str(confidence / 100) + ' \n')
else:
pass
def import_results_neo(input_file='result.txt', results_file='results.txt', obj_names='/home/as-hunt/Etra-Space/white-thirds/obj.names'):
'''Import's Yolo darknet detection results and filters bouding boxes that are outside of the image dimensions
This function will use the index given to darknet when training the model to determine the class of the object
Args:
input_file (str): The path to the results file
results_file (str): The path to the output file
obj_names (str): The path to the obj.names file
'''
arry = []
res = open(results_file, 'w')
with open(obj_names, 'r') as f:
for line in f:
arry.append(line.rstrip())
with open(input_file, 'r') as f:
for line in f:
if line[0:4] == '/hom':
lin = re.split('/| ', line)
li = filter(lambda a: '.jpg' in a, lin)
l = list(li)[0][:-5]
image_name = l
elif (line[0:3] in arry) or (line[0:4] in arry ) == True:
print(line[0:3])
lin = re.split(':|%|t|w|h', line)
classes = int(arry.index(lin[0]))
confidence = int((lin[1]))
if int(lin[4]) < 0:
left_x = 0
else:
left_x = int(lin[4])
if int(lin[6]) < 0:
top_y = 0
else:
top_y = int(lin[6])
width = int(lin[10])
height = int(lin[14][:-2])
bottom_y = top_y + height
right_x = left_x + width
res.write(image_name + ' ' + str(classes) + ' ' + str(left_x) + ' ' + str(top_y) + ' ' + str(right_x) + ' ' + str(bottom_y) + ' ' + str(confidence / 100) + ' \n')
else:
pass
def make_groud_truth(ground_truth_file='gt.txt', test_folder='/home/as-hunt/Etra-Space/new_data_sidless/valid/'):
gt_file = open(ground_truth_file, 'w')
for file in os.listdir(test_folder):
if file.endswith('.txt'):
if file == 'test.txt':
pass
elif file == 'classes.txt':
pass
elif file == 'train.txt':
pass
elif file == 'valid.txt':
pass
elif file == 'ground_truth.txt':
pass
elif file == 'result_run_1.txt':
pass
elif file == 'result_run_2.txt':
pass
else:
img_name = file[:-4]
count = 0
annot = open(test_folder + file, 'r+')
for line in annot:
lin = re.split(' ', line)
# print(lin[1])
classes = lin[0]
center_x = lin[1]
center_y = lin[2]
width = lin[3]
height = lin[4]
if center_x == '0':
# print('center_x ('+ center_x +') is 0')
pass
elif center_y == '0':
# print('center_x ('+ center_y +') is 0')
pass
elif width == '0':
# print('widht ('+ width +') is 0')
pass
elif height == '0':
# print('height ('+ height +') is 0')
pass
else:
center_x = decimal.Decimal(center_x) * 416
center_y = decimal.Decimal(center_y) * 416
width = decimal.Decimal(width) * 416
height = decimal.Decimal(height) * 416
left_x = int(decimal.Decimal(center_x) - (width / 2))
top_y = int(decimal.Decimal(center_y) - (height / 2))
right_x = int(decimal.Decimal(center_x) + (width / 2))
bottom_y = int(decimal.Decimal(center_y) + (height / 2))
if left_x <= 6:
# print('left_x (' + str(left_x) + ') is less than 6')
pass
elif left_x >= 410:
# print('left_x (' + str(left_x) + ') is greater than 410')
pass
else:
if top_y <= 6:
# print('top_y (' + str(top_y) + ') is less than 6')
pass
elif top_y >= 410:
print('top_y (' + str(top_y) + ') is greater than 410')
pass
else:
if right_x <= 6:
print('right_x (' + str(right_x) + ') is less than 6')
pass
elif right_x >= 410:
print('right_x (' + str(right_x) + ') is greater than 410')
pass
else:
if bottom_y <= 6:
print('bottom_y (' + str(bottom_y) + ') is less than 6')
pass
elif bottom_y >= 410:
print('bottom_y (' + str(bottom_y) + ') is greater than 410')
pass
else:
gt_file.write(img_name + ' ' + str(classes) + ' ' + str(left_x) + ' ' + str(top_y) + ' ' + str(right_x) + ' ' + str(bottom_y) + ' \n')
count += 1
# print('Line ' + str(count) + ': ' + img_name + ' ' + str(classes) + ' ' + str(left_x) + ' ' + str(top_y) + ' ' + str(right_x) + ' ' + str(bottom_y))
def make_groud_truth_unfiltered(ground_truth_file='gt.txt', test_folder='/home/as-hunt/Etra-Space/new_data_sidless/valid/'):
gt_file = open(ground_truth_file, 'w')
for file in os.listdir(test_folder):
if file.endswith('.txt'):
if file == 'test.txt':
pass
elif file == 'classes.txt':
pass
elif file == 'train.txt':
pass
elif file == 'valid.txt':
pass
elif file == 'ground_truth.txt':
pass
elif file == 'result_run_1.txt':
pass
elif file == 'result_run_2.txt':
pass
else:
img_name = file[:-4]
count = 0
annot = open(test_folder + file, 'r+')
for line in annot:
lin = re.split(' ', line)
# print(lin[1])
classes = lin[0]
center_x = lin[1]
center_y = lin[2]
width = lin[3]
height = lin[4]
if center_x == '0':
# print('center_x ('+ center_x +') is 0')
pass
elif center_y == '0':
# print('center_x ('+ center_y +') is 0')
pass
elif width == '0':
# print('widht ('+ width +') is 0')
pass
elif height == '0':
# print('height ('+ height +') is 0')
pass
else:
center_x = decimal.Decimal(center_x) * 416
center_y = decimal.Decimal(center_y) * 416
width = decimal.Decimal(width) * 416
height = decimal.Decimal(height) * 416
left_x = int(decimal.Decimal(center_x) - (width / 2))
top_y = int(decimal.Decimal(center_y) + (height / 2))
right_x = int(decimal.Decimal(center_x) + (width / 2))
bottom_y = int(decimal.Decimal(center_y) - (height / 2))
gt_file.write(img_name + ' ' + str(classes) + ' ' + str(left_x) + ' ' + str(top_y) + ' ' + str(right_x) + ' ' + str(bottom_y) + ' \n')
count += 1
# print('Line ' + str(count) + ': ' + img_name + ' ' + str(classes) + ' ' + str(left_x) + ' ' + str(top_y) + ' ' + str(right_x) + ' ' + str(bottom_y))
def count_classes(test_folder='/home/as-hunt/Etra-Space/new_data_sidless/valid/', chart=False, chart_name='chart.png', labs=['1', '2', '3']):
class_1 = 0
class_2 = 0
class_3 = 0
class_4 = 0
class_5 = 0
class_6 = 0
for file in os.listdir(test_folder):
if file.endswith('.txt'):
if file == 'test.txt':
pass
elif file == 'classes.txt':
pass
elif file == 'train.txt':
pass
elif file == 'valid.txt':
pass
elif file == 'ground_truth.txt':
pass
elif file == 'result_run_1.txt':
pass
elif file == 'result_run_2.txt':
pass
elif file == '_darknet.labels':
pass
else:
img_name = file[:-4]
count = 0
annot = open(test_folder + file, 'r+')
for line in annot:
lin = re.split(' ', line)
# print(lin[1])
classes = lin[0]
if classes == '0':
class_1 += 1
elif classes == '1':
class_2 += 1
elif classes == '2':
class_3 += 1
elif classes == '3':
class_4 += 1
elif classes == '4':
class_5 += 1
elif classes == '5':
class_6 += 1
print('class_1: ' + str(class_1))
print('class_2: ' + str(class_2))
print('class_3: ' + str(class_3))
print('class_4: ' + str(class_4))
print('class_5: ' + str(class_5))
print('class_6: ' + str(class_6))
if chart == True:
labels = labs
plt.figure(figsize = (10,7))
plt.title(chart_name[:-4])
if len(labels) == 2:
count = [class_1, class_2]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
elif len(labels) == 3:
count = [class_1, class_2, class_3]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
elif len(labels) == 4:
count = [class_1, class_2, class_3, class_4]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
elif len(labels) == 5:
count = [class_1, class_2, class_3, class_4, class_5]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
elif len(labels) == 6:
count = [class_1, class_2, class_3, class_4, class_5, class_6]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
plt.savefig(chart_name, bbox_inches='tight')
def count_classes_file(test_file='/home/as-hunt/Etra-Space/new_data_sidless/gt.txt', chart=False, chart_name='chart.png', labs=['1', '2', '3']):
class_1 = 0
class_2 = 0
class_3 = 0
class_4 = 0
class_5 = 0
class_6 = 0
count = 0
annot = open(test_file, 'r+')
for line in annot:
lin = re.split(' ', line)
# print(lin[1])
classes = lin[1]
if classes == '0':
class_1 += 1
elif classes == '1':
class_2 += 1
elif classes == '2':
class_3 += 1
elif classes == '3':
class_4 += 1
elif classes == '4':
class_5 += 1
elif classes == '5':
class_6 += 1
print('class_1: ' + str(class_1))
print('class_2: ' + str(class_2))
print('class_3: ' + str(class_3))
print('class_4: ' + str(class_4))
print('class_5: ' + str(class_5))
print('class_6: ' + str(class_6))
if chart == True:
labels = labs
plt.figure(figsize = (10,7))
plt.title(chart_name[:-4])
if len(labels) == 2:
count = [class_1, class_2]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
elif len(labels) == 3:
count = [class_1, class_2, class_3]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
elif len(labels) == 4:
count = [class_1, class_2, class_3, class_4]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
elif len(labels) == 5:
count = [class_1, class_2, class_3, class_4, class_5]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
elif len(labels) == 6:
count = [class_1, class_2, class_3, class_4, class_5, class_6]
fig, ax = plt.subplots()
ax.pie(count, labels=labels, autopct='%1.1f%%', shadow=True, startangle=90)
plt.savefig(chart_name, bbox_inches='tight')