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changed cython to noumpy in modify_image #4

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changed cython to noumpy in modify_image #4

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90 changes: 24 additions & 66 deletions PythonClient/tools_and_scripts/prepare_training_data.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,9 +7,7 @@
import struct
import csv
from tqdm import tqdm

import multiprocessing as mp
from functools import partial
from datetime import datetime

def get_total_screenfiles(dir):
count = 0
Expand Down Expand Up @@ -78,7 +76,6 @@ def make_yt_vid(input_dir, output_dir):
# for file in files:
# print(os.path.join(subdir, file))


def read_pfm(filename):
with Path(filename).open('rb') as pfm_file:
line1, line2, line3 = (pfm_file.readline().decode('latin-1').strip() for _ in range(3))
Expand Down Expand Up @@ -198,6 +195,8 @@ def make_numpy_npz(input_dir, output_dir, target_type=np.float16, inspect=False,
def _get_color_list_for_stencils_transforms():
# transforms from my samples (list1) to epe format (list2)

# TODO: dictionary maybe

list1, list2 = [], []
# undefined (mostly street furniture)
list1.append((255, 255, 255))
Expand Down Expand Up @@ -255,32 +254,29 @@ def _get_color_list_for_stencils_transforms():

return list1, list2

def process_tile(img_buff, list1, list2, tile_size, modify_image, tile_index):
def modify_image(img_buff, list1, list2):
start = datetime.now()
for i in range(len(list1)):
img_buff = np.where(img_buff == list1[i], list2[i], img_buff)
end = datetime.now()
time = end - start
print(time)

#niepotrzebne
def process_tile(img_buff, tile_index, list1, list2, tile_size, modify_image):
i, j = tile_index
tile = img_buff[i:i+tile_size, j:j+tile_size]
modify_image(tile, list1, list2)
return tile, tile_index

def stencil_rgb_to_gray(input_dir, output_dir):
# pip install Cython
# python setup_epe_stencils_cython.py build_ext --inplace
from get_epe_stencils import modify_image

screenshot_w = 3840
screenshot_h = 2160

def stencil_rgb_to_gray(input_dir, output_dir):
buffers_of_interest_255rgb = ['5'] # '5', '10'

rootdir = input_dir

list_my, list_epe = _get_color_list_for_stencils_transforms()

# Define the size of the square tiles for parallel processing
tile_size = 256

# Define the number of worker processes to use
num_processes = 12

# rootdir = os.fsencode(rootdir)
for parent_dir in tqdm(os.listdir(rootdir)):
# filename = os.fsdecode(file)
Expand All @@ -302,33 +298,10 @@ def stencil_rgb_to_gray(input_dir, output_dir):
else:
img_buff = cv2.imread(file_to_read_path)
img_buff = cv2.cvtColor(img_buff, cv2.COLOR_BGR2RGB)
print("buff shp: ", img_buff.shape)

# Create a list of tile indices
tile_indices = [(i, j) for i in range(0, img_buff.shape[0], tile_size) for j in
range(0, img_buff.shape[1], tile_size)]

# Create a multiprocessing pool with the specified number of processes
pool = mp.Pool(num_processes)

# Process each tile in parallel using the multiprocessing pool
# pool.map(process_tile, tile_indices)
# Create a partial function with the fixed arguments
# Cython-based speed up
# @todo in python it is super non-efficient as each process will copy the content
# but probably it is faster doing it like that anyway that using numpy broadcasting in
# mask list.
partial_process_tile = partial(process_tile, img_buff=img_buff, list1=list_my,
list2=list_epe, tile_size=tile_size,
modify_image=modify_image)

results = pool.map(partial_process_tile, tile_indices)

for tile, tile_index in results:
i, j = tile_index
img_buff[i:i + tile_size, j:j + tile_size] = tile

# Close the multiprocessing pool
pool.close()
#self._get_color_list_for_stencils_transforms()
modify_image(img_buff, list1=list_my, list2=list_epe)

# img_buff = cv2.resize(img_buff, (screenshot_w, screenshot_h))
assert (len(img_buff.shape) == 3)
Expand All @@ -337,14 +310,14 @@ def stencil_rgb_to_gray(input_dir, output_dir):
output_file_path = os.path.join(output_dir, parent_dir, "gray_stencils", file_to_pick)
cv2.imwrite(output_file_path, img_buff[:, :, 0])

def create_training_txt(fake_path, real_path):
def create_training_txt():
data_dict = {
'fake' : fake_path,
'real' : real_path,
"/mnt/d/Kamil/data_collected/airsim_drone/dataset_coffing": "fake",
"/mnt/d/Kamil/data_collected/first_vids_matrix_like_real": "real",
# Add more paths and types to the dictionary as needed
}

for data_type, path in data_dict.items():
for path, data_type in data_dict.items():
print(f"Processing: {path}")

if data_type == "fake":
Expand Down Expand Up @@ -447,7 +420,7 @@ def convert_to_windows_paths():
print(f"Converted {csv_file} to {new_filename}")

def drop_alpha_channels_in_screenshots():
patches = ["/mnt/d/Kamil/data_collected/airsim_drone/dataset_coffing"] # Replace with your list of patch paths
patches = ["/mnt/c/users/Maciej/downloads/dataset_two_example_samples"] # Replace with your list of patch paths

for patch_path in patches:
subdirectories = os.listdir(patch_path)
Expand Down Expand Up @@ -536,7 +509,7 @@ def deprecated_create_training_txt(input_dir, output_dir):
def resize_msegs():
patches = [
# "/mnt/d/Kamil/data_collected/airsim_drone/dataset_coffing",
"/mnt/d/Kamil/data_collected/first_vids_matrix_like_real/real_training_v002/upscale_msegs",
"/mnt/c/users/Maciej/downloads/dataset_two_example_samples/",
# Add more patch paths as needed
]

Expand Down Expand Up @@ -634,8 +607,6 @@ def main(args):
do_resize_msegs = args["resize_msegs"]
do_drop_alpha_channels = args["drop_alpha_channel"]
do_convert_to_win_paths = args["windows_convert_paths"]
fake_input_dir = args["fake_input_dir"]
real_input_dir = args["real_input_dir"]

if resize:
resize_screenshots(input_dir, output_dir)
Expand All @@ -646,8 +617,7 @@ def main(args):
if stencil_to_gray:
stencil_rgb_to_gray(input_dir, output_dir)
if do_create_training_txt:
# @todo this possibly will be refactored to separate script on EPE site
create_training_txt(fake_path=fake_input_dir, real_path=real_input_dir)
create_training_txt()
if do_resize_msegs:
resize_msegs()
if do_drop_alpha_channels:
Expand All @@ -665,18 +635,6 @@ def main(args):
help="folder with separate recorded data runs",
default="D:/Kamil/data_collected/airsim_drone/",
)
parser.add_argument(
"--fake_input_dir",
type=str,
help="folder with separate recorded data runs",
default="/mnt/d/Kamil/data_collected/airsim_drone/dataset_coffing",
)
parser.add_argument(
"--real_input_dir",
type=str,
help="folder with separate recorded data runs",
default="/mnt/d/Kamil/data_collected/first_vids_matrix_like_real/real_training_v002",
)
parser.add_argument(
"--output_dir",
type=str,
Expand Down