| import os |
|
|
| 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) |
| |
| |
| 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 |
| self.output_path = output_path |
| 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 |
| ) |
| |
|
|
| |
| 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] |
| |
| 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) |
| |
| |
| 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}') |
|
|
