File size: 7,028 Bytes
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from .utils import init_submodules, save_json, load_json
import importlib
from itertools import chain
from pathlib import Path
import shutil
from PIL import Image
import pandas as pd
def frames2gif(source_folder):
output_folder = os.path.join(source_folder, "tempt_dir")
os.makedirs(output_folder, exist_ok=True)
images = []
for file_name in sorted(os.listdir(source_folder)):
file_path = os.path.join(source_folder, file_name)
if os.path.isfile(file_path) and file_name.lower().endswith(('.png', '.jpg', '.jpeg')):
img = Image.open(file_path)
images.append(img)
# print(file_name)
if images:
folder_name = os.path.basename(source_folder)
gif_path = os.path.join(output_folder, f"{folder_name}.gif")
images[0].save(gif_path, save_all=True, append_images=images[1:], optimize=False, duration=500, loop=0)
for img in images:
img.close()
else:
raise Exception("No images found in the source folder.")
return output_folder
class EditBoard(object):
def __init__(self, device, output_path):
self.device = device # cuda or cpu
self.output_path = output_path # output directory to save EditBoard results
os.makedirs(self.output_path, exist_ok=True)
def build_metadata_json_single(
self, original_video_path, edited_video_path, semantic_mask_path,
source_prompt, target_prompt,
dimension_list, name
):
cur_full_info_list=[]
temp = {
k: v for k, v in {
"original_video_path": original_video_path,
"edited_video_path": edited_video_path,
"semantic_mask_path": semantic_mask_path,
"source_prompt": source_prompt,
"target_prompt": target_prompt,
"dimension": dimension_list,
}.items() if v is not None
}
cur_full_info_list.append(temp)
cur_full_info_path = os.path.join(self.output_path, name+'_metadata.json')
save_json(cur_full_info_list, cur_full_info_path)
print(f'Evaluation metadata saved to {cur_full_info_path}')
return cur_full_info_path
def build_metadata_json_multi(self, dimension_list, name, script):
cur_full_info_list = []
if script.split(".")[-1] == 'xlsx':
df = pd.read_excel(script)
elif script.split(".")[-1] == 'csv':
df = pd.read_csv(script)
else:
raise Exception("Prompt file must be excel or csv!")
available_columns = set(df.columns)
expected_columns = {
"original_video_path": "original_video_path",
"edited_video_path": "edited_video_path",
"semantic_mask_path": "semantic_mask_path",
"source_prompt": "source_prompt",
"target_prompt": "target_prompt"
}
for index, row in df.iterrows():
temp = {}
for col_key, json_key in expected_columns.items():
if col_key in available_columns and pd.notna(row[col_key]):
temp[json_key] = row[col_key]
temp["dimension"] = dimension_list
cur_full_info_list.append(temp)
cur_full_info_path = os.path.join(self.output_path, name + '_metadata.json')
save_json(cur_full_info_list, cur_full_info_path)
print(f'Evaluation metadata saved to {cur_full_info_path}')
return cur_full_info_path
def evaluate(
self, original_video_path, edited_video_path, semantic_mask_path,
source_prompt, target_prompt,
dimension_list, name, script
):
read_frame = False
results_dict = {}
if dimension_list is None:
raise Exception("Dimension can't be none!")
submodules_dict = init_submodules(dimension_list, read_frame=read_frame)
if script == None:
print("Using Normal Command!")
cur_full_info_path = self.build_metadata_json_single(
original_video_path, edited_video_path, semantic_mask_path,
source_prompt, target_prompt,
dimension_list, name
)
else:
print("Using Script Command!")
cur_full_info_path = self.build_metadata_json_multi(
dimension_list, name, script
)
# Start calculating
flag = False
metadata = load_json(cur_full_info_path)
gif_list = []
if any(dimension in dimension_list for dimension in ['subject_consistency', 'background_consistency', 'aesthetic_quality', 'imaging_quality']):
flag = True
for i in metadata:
gif_path = frames2gif(i["edited_video_path"])
gif_list.append(gif_path)
for dimension in dimension_list:
print(f"Calculating {dimension} ...")
try:
dimension_module = importlib.import_module(f'editboard.{dimension}')
evaluate_func = getattr(dimension_module, f'compute_{dimension}')
except Exception as e:
raise NotImplementedError(f'UnImplemented dimension {dimension}!, {e}')
submodules_list = submodules_dict[dimension]
# print(f'cur_full_info_path: {cur_full_info_path}') # TODO: to delete
results = evaluate_func(cur_full_info_path, self.device, submodules_list)
results_dict[dimension] = results
if flag:
for i in gif_list:
shutil.rmtree(i)
# Finish calculating
for i in metadata:
i["dimension"] = dict()
for dimension in dimension_list:
if dimension in ['subject_consistency', 'background_consistency', 'aesthetic_quality', 'imaging_quality']:
i["dimension"][dimension] = results_dict[dimension][i["edited_video_path"]]
elif dimension in ["ff_alpha", "ff_beta"]:
i["dimension"][dimension] = results_dict[dimension][i["original_video_path"] + i["edited_video_path"]]
elif dimension in ["clip_similarity", "success_rate"]:
i["dimension"][dimension] = results_dict[dimension][i["edited_video_path"] + i["source_prompt"] + i["target_prompt"]]
elif dimension in ["semantic_score"]:
i["dimension"][dimension] = results_dict[dimension][i["original_video_path"] + i["edited_video_path"] + i["semantic_mask_path"]]
else:
raise Exception("Wrong dimension!")
output_name = os.path.join(self.output_path, name+'_eval_results.json')
save_json(metadata, output_name)
print('All Done!')
print(f'Evaluation results saved to {output_name}')
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