/
prepare_images.py
107 lines (97 loc) · 4.91 KB
/
prepare_images.py
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from pyproj import Proj
from edit_exif import get_gps_location
import numpy as np
import pandas as pd
import rawpy
import imageio
import generate_sky_masks as gsm
import videos_to_colmap as v2c
import colmap_util as ci
def extract_gps_and_path(individual_pictures, colmap_img_root, system, centroid=None, **env):
proj = Proj(system)
georef_list = []
for img in individual_pictures:
gps = get_gps_location(colmap_img_root / img)
if gps is not None:
lat, lng, alt = gps
x, y = proj(lng, lat)
if centroid is None:
centroid = np.array([x, y, alt])
x -= centroid[0]
y -= centroid[1]
alt -= centroid[2]
georef_list.append("{} {} {} {}\n".format(img, x, y, alt))
return georef_list, centroid
def extract_pictures_to_workspace(input_folder, colmap_img_root, individual_pictures_path, workspace, colmap,
raw_ext, pic_ext, more_sift_features, generic_model, **env):
picture_folder = input_folder / "Pictures"
pictures = []
raw_files = sum((list(picture_folder.walkfiles('*{}'.format(ext))) for ext in raw_ext), [])
for raw in raw_files:
if not any((raw.stripext() + ext).isfile() for ext in pic_ext):
converted_pic_path = raw.relpath(picture_folder).stripext() + '.jpg'
if not converted_pic_path.isfile():
raw_array = rawpy.imread(raw)
rgb = raw_array.postprocess()
dst = individual_pictures_path / converted_pic_path
dst.parent.makedirs_p()
imageio.imsave(dst, rgb)
pictures.append(converted_pic_path)
normal_files = sum((list(picture_folder.walkfiles('*{}'.format(ext))) for ext in pic_ext), [])
for file in normal_files:
pic_path = file.relpath(picture_folder)
if not pic_path.isfile():
dst = individual_pictures_path / pic_path
dst.parent.makedirs_p()
file.copy(dst)
pictures.append(colmap_img_root.relpathto(individual_pictures_path) / pic_path)
gsm.process_folder(folder_to_process=individual_pictures_path, colmap_img_root=colmap_img_root, pic_ext=pic_ext, **env)
with open(picture_folder / "individual_pictures.txt", 'w') as f:
f.write("\n".join(pictures) + "\n")
colmap.extract_features(per_sub_folder=True, model=generic_model,
image_list=picture_folder/"individual_pictures.txt", more=more_sift_features)
return pictures
def extract_videos_to_workspace(video_path, video_frame_list_thorough, georef_frames_list, **env):
existing_georef, env["centroid"] = extract_gps_and_path(**env)
if env["full_metadata"] is None:
env["full_metadata"] = env["workspace"] / "full_metadata.csv"
if env["full_metadata"].isfile():
existing_metadata = pd.read_csv(env["full_metadata"])
else:
existing_metadata = None
path_lists, extracted_video_folders, full_metadata = v2c.process_video_folder(output_video_folder=video_path,
existing_georef=existing_georef,
existing_metadata=existing_metadata,
**env)
if path_lists is not None:
full_metadata.to_csv(env["full_metadata"])
with open(video_frame_list_thorough, "w") as f:
f.write("\n".join(path_lists["thorough"]["frames"]))
with open(georef_frames_list, "w") as f:
f.write("\n".join(existing_georef) + "\n")
f.write("\n".join(path_lists["thorough"]["georef"]) + "\n")
for v, video_folder in extracted_video_folders.items():
with open(video_folder / "lowfps.txt", "w") as f:
f.write("\n".join(path_lists[v]["frames_lowfps"]) + "\n")
with open(video_folder / "georef.txt", "w") as f:
f.write("\n".join(existing_georef) + "\n")
f.write("\n".join(path_lists["thorough"]["georef"]) + "\n")
f.write("\n".join(path_lists[v]["georef_lowfps"]) + "\n")
for j, l in enumerate(path_lists[v]["frames_full"]):
with open(video_folder / "full_chunk_{}.txt".format(j), "w") as f:
f.write("\n".join(l) + "\n")
gsm.process_folder(folder_to_process=video_path, **env)
return extracted_video_folders
def choose_biggest_model(dir):
colmap_model_dirs = dir.dirs("[0-9]*")
model_sizes = [len(ci.read_model.read_images_binary(d/"images.bin")) for d in colmap_model_dirs]
return colmap_model_dirs[model_sizes.index(max((model_sizes)))]
def group_pics_by_folder(pictures):
result = {}
for p in pictures:
key = p.parent
if p.parent not in result.keys():
result[key] = [p]
else:
result[key].append(p)
return result