| import os |
| import math |
| import numpy as np |
| import pandas as pd |
|
|
|
|
| annotations_file_name = "surgeons_annotations" |
| truncation_ratio = 0.85 |
| target_fps = 5 |
|
|
|
|
| video_fps = 25 |
| doctors_annotations = pd.read_excel(f"../data/{annotations_file_name}.xlsx", engine='openpyxl') |
| result_path = f"../data/{annotations_file_name.split('.')[0]}" |
| index_data = pd.read_csv("../results/videos_frames_index.csv") |
|
|
| for video_tag, video_data in doctors_annotations.groupby("video"): |
| print(f"Processing video {video_tag}") |
| video_name = f"video{str(video_tag).zfill(2)}" |
| valid_frames = index_data[index_data["video_name"] == video_name]["final_frame"].iloc[0] |
|
|
| video_result = pd.DataFrame(columns=["video_name", "image", "two_structures_score", "cystic_plate_score", |
| "hc_triangle_score"]) |
| video_result["image"] = list(range(valid_frames + 1)) |
| video_result[["video_name", "two_structures_score", "cystic_plate_score", |
| "hc_triangle_score"]] = [video_name, 0, 0, 0] |
|
|
| for i, row in video_data.iterrows(): |
| if row["two_structures"] + row["cystic_plate"] + row["hepatocystic_triangle"] > 0: |
| tag = [row["two_structures"], row["cystic_plate"], row["hepatocystic_triangle"]] |
| initial_frame = int(row["initial_minute"] * 60 * video_fps + row["initial_second"] * video_fps) |
| final_frame = int(row["final_minute"] * 60 * video_fps + row["final_second"] * video_fps) |
| assert initial_frame <= video_result.shape[0], "ERROR: initial frame is out of range" |
| if final_frame >= video_result.shape[0]: |
| final_frame = valid_frames |
| for frame_number in range(initial_frame, final_frame+1): |
| video_result.loc[frame_number, ["two_structures_score", |
| "cystic_plate_score", "hc_triangle_score"]] = tag |
|
|
| |
|
|
| initial_second = math.floor(video_result.shape[0] * truncation_ratio / 25) |
| initial_frame = initial_second * 25 |
| video_result = video_result[initial_frame:] |
|
|
| |
|
|
| chunk_list = list() |
| for offset in range(0, video_result.shape[0], video_fps): |
| chunk = video_result[offset:offset+video_fps] |
| chunk.reset_index(inplace=True, drop=True) |
| replace_flag = False |
| if chunk.shape[0] < target_fps: |
| replace_flag = True |
| index = list(np.random.choice(chunk.shape[0], target_fps, replace=replace_flag)) |
| chunk = chunk.iloc[index] |
| chunk_list.append(chunk) |
|
|
| video_result = pd.concat(chunk_list) |
| video_result.to_csv(os.path.join(result_path, f"{video_name}.csv"), index=False) |
