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confirm_length.py
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confirm_length.py
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import cv2
import os
from configs import config_preprocessing as config
from base.utils import load_single_pkl, txt_row_count
import pandas as pd
import numpy as np
def get_video_path(label, partition):
label = label.split(".txt")[0]
video_filename_without_extension = label
# Find the corresonding video filename.
if video_filename_without_extension.endswith("_right"):
video_filename_without_extension = video_filename_without_extension.split("_right")[0]
elif video_filename_without_extension.endswith("_left"):
video_filename_without_extension = video_filename_without_extension.split("_left")[0]
if (video_filename_without_extension + ".avi") in video_to_partition_dict[partition]:
corresponding_video_filename = video_filename_without_extension + ".avi"
elif (video_filename_without_extension + ".mp4") in video_to_partition_dict[partition]:
corresponding_video_filename = video_filename_without_extension + ".mp4"
else:
print(video_filename_without_extension)
raise ValueError("Cannot find the corresponding video")
corresponding_video = os.path.join(raw_video_path, corresponding_video_filename)
return corresponding_video
raw_video_path = config['raw_video_path']
annotation_path = config['annotation_path']
dataset_info =load_single_pkl(r"H:\Affwild2_processed", "dataset_info")
video_to_partition_dict = dataset_info['video_to_partition_dict']
annotation_to_partition_dict = dataset_info['annotation_to_partition_dict']
available_frame_indices = load_single_pkl(r"H:\Affwild2_processed", "available_frame_indices")
labeled_frame_indices = load_single_pkl(r"H:\Affwild2_processed", "labeled_frame_indices")
count = 1
result = []
for partition, trials in annotation_to_partition_dict.items():
if partition == "Target_Set":
for trial in trials:
trial_name = trial
current_annotation_path = os.path.join(annotation_path, partition, trial)
current_video_path = get_video_path(trial, partition)
video = cv2.VideoCapture(current_video_path)
num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
num_considered = np.count_nonzero(np.asarray(available_frame_indices[partition][trial]) == 1)
difference = num_frames - num_considered
result.append([trial_name, num_frames, num_considered, difference])
else:
for trial in trials:
trial_name = trial[:-4]
current_annotation_path = os.path.join(annotation_path, partition, trial)
current_video_path = get_video_path(trial, partition)
video = cv2.VideoCapture(current_video_path)
num_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
num_considered = np.count_nonzero(np.asarray(available_frame_indices[partition][trial_name]) == 1)
difference = num_frames - num_considered
result.append([trial_name, num_frames, num_considered, difference])
# string = "{}: num_frames = {:d}, num_rows = {:d}, num_inequal = {:d}".format(trial_name, num_frames, num_labels, count)
# print(string)
result = np.stack(result)
df = pd.DataFrame(result, columns=["trial name", "frame_count", "considered_count", "difference"])
df.to_csv("length.csv")
print(0)