120 lines
3.9 KiB
Python
120 lines
3.9 KiB
Python
from PIL import Image
|
|
import os
|
|
import subprocess
|
|
import shutil
|
|
|
|
backgrounds_file_path = "backgrounds.txt"
|
|
info_base_path = r"./info"
|
|
negatives_path = r"./negatives"
|
|
positives_path = r"./positives"
|
|
training_data_base = r"./training_data_"
|
|
|
|
opencv_path = r".\opencv\build\x64\vc15\bin\opencv_createsamples.exe"
|
|
|
|
set_sizes = [1, 2, 5, 10]
|
|
|
|
max_xangle = 0.5
|
|
max_yangle = 0.5
|
|
max_zangle = 0.5
|
|
|
|
w, h = 25, 18
|
|
|
|
class InfoEntry:
|
|
info_lst_line: str
|
|
image_path: str
|
|
|
|
def __init__(self, info_line, file_path):
|
|
self.info_lst_line = info_line
|
|
self.image_path = file_path
|
|
|
|
def __str__(self):
|
|
return f"Image Entry: {self.info_lst_line}, {self.image_path}"
|
|
|
|
|
|
max_x = 750
|
|
max_y = 800
|
|
|
|
# remove too small images
|
|
for image in os.listdir("./negatives"):
|
|
im = Image.open(f"./negatives/{image}")
|
|
width, height = im.size
|
|
del im
|
|
if width <= max_x:
|
|
os.remove(f"./negatives/{image}")
|
|
elif height <= max_y:
|
|
os.remove(f"./negatives/{image}")
|
|
|
|
# remove any existing file and assume old data
|
|
if os.path.exists(backgrounds_file_path):
|
|
os.remove(backgrounds_file_path)
|
|
|
|
# regenerate the available negatives list
|
|
count_negatives = len(os.listdir(negatives_path))
|
|
for img in os.listdir(negatives_path):
|
|
line = f"{negatives_path}/" + img + "\n"
|
|
with open(backgrounds_file_path, 'a') as f:
|
|
f.write(line)
|
|
|
|
info_dirs = []
|
|
|
|
if len(os.listdir(positives_path)) > max(set_sizes):
|
|
print("Your set sizes were larger than the available positive images!")
|
|
quit(2)
|
|
|
|
for img in os.listdir(positives_path):
|
|
i = len(info_dirs)
|
|
info_dir = f"{info_base_path}{i}"
|
|
|
|
com = f"{opencv_path} -img positives/" + str(i) + ".png -bg backgrounds.txt -info " + info_dir + "/info.lst" + \
|
|
" -pngoutput " + info_dir + " -maxxangle " + str(max_xangle) + " -maxyangle " + str(max_yangle) + " -maxzangle " + str(max_zangle) + \
|
|
" -num " + str(count_negatives)
|
|
|
|
if not os.path.exists(info_dir):
|
|
subprocess.call(com, shell=True)
|
|
|
|
info_dirs.append(info_dir)
|
|
|
|
for i in set_sizes:
|
|
if not os.path.exists(training_data_base + str(i)):
|
|
os.makedirs(training_data_base + str(i))
|
|
|
|
def join_info_folders(info_dirs: list, output_dir: str):
|
|
info_dir: str
|
|
cur_entry_name = 0
|
|
for info_dir in info_dirs:
|
|
info_lines = []
|
|
with open(info_dir + "/info.lst", 'r') as info_file:
|
|
for line in info_file.readlines():
|
|
image_path = f"{info_dir}/{line.split(' ')[0]}"
|
|
info_lines.append(InfoEntry(line.strip(), image_path))
|
|
|
|
item: InfoEntry
|
|
for item in info_lines:
|
|
shutil.copy(item.image_path, f"{output_dir}/{str(cur_entry_name)}.jpg")
|
|
with open(f"{output_dir}/info.lst", 'a') as info_file:
|
|
to_write = []
|
|
to_write.append(str(cur_entry_name) + ".jpg")
|
|
to_write = to_write + item.info_lst_line.split(" ")[1:]
|
|
to_write.append("\n")
|
|
info_file.write(" ".join(to_write))
|
|
cur_entry_name += 1
|
|
|
|
for i in set_sizes:
|
|
join_info_folders(info_dirs[:i], training_data_base + str(i))
|
|
|
|
commands = []
|
|
|
|
for i in set_sizes:
|
|
num_positives = len(os.listdir(training_data_base + str(i)))
|
|
if os.path.exists(training_data_base + str(i) + ".vec"):
|
|
os.remove(training_data_base + str(i) + ".vec")
|
|
com = f"{opencv_path} -info {training_data_base + str(i)}\info.lst -num {num_positives} -w {w} -h {h} -vec {training_data_base + str(i)}.vec"
|
|
subprocess.call(com, shell=True)
|
|
commands.append(f".\opencv\\build\\x64\\vc15\\bin\opencv_traincascade.exe -data data_{str(i)} -vec .\\{training_data_base + str(i)}.vec -bg .\\{backgrounds_file_path} -numPos {num_positives} -numNeg {num_positives / 2} -numStages 15 -w {w} -h {h}")
|
|
|
|
if not os.path.exists(".\data_" + str(i)):
|
|
os.makedirs(".\data_" + str(i))
|
|
|
|
for i in commands:
|
|
print(f"You are ready to train the models with: \n {i}")
|
|
|