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# Importing the requirements
from PIL import Image
from decord import VideoReader, cpu
import re
# Maximum number of frames to use
MAX_NUM_FRAMES = 15 # If CUDA OOM, set a smaller number
def parse_string(string, tags):
"""
Extracts the content between the specified HTML tags from the given string.
Args:
string (str): The input string to search for the tag content.
tags (list): A list of HTML tags to search for.
Returns:
dict: A dictionary with tags as keys and lists of content as values.
Example:
>>> parse_string("<code>Hello, World!</code><note>Important</note>", ["code", "note"])
{'code': ['Hello, World!'], 'note': ['Important']}
"""
results = {}
for tag in tags:
pattern = rf"<{tag}>(.*?)</{tag}>"
matches = re.findall(pattern, string, re.DOTALL)
results[tag] = matches if matches else None
return results
def parse_annotations(annotations_list):
"""
Converts a list of annotations into a dictionary of key-value pairs.
Args:
annotations_list (list): A list of annotations in the format 'key: value'.
Returns:
dict: A dictionary with annotation keys and values.
"""
annotations_dict = {}
for annotation in annotations_list:
key, value = annotation.split(': ')
annotations_dict[key] = int(value)
return annotations_dict
def encode_video(video_path):
"""
Encodes a video file into a list of frames.
Args:
video_path (str): The path to the video file.
Returns:
list: A list of frames, where each frame is represented as an Image object.
"""
def uniform_sample(l, n):
"""
Uniformly samples elements from a list.
Args:
- l (list): The input list.
- n (int): The number of elements to sample.
Returns:
list: A list of sampled elements.
"""
gap = len(l) / n
idxs = [int(i * gap + gap / 2) for i in range(n)]
return [l[i] for i in idxs]
# Read the video file and sample frames
vr = VideoReader(video_path, ctx=cpu(0))
sample_fps = round(vr.get_avg_fps() / 1) # FPS
frame_idx = [i for i in range(0, len(vr), sample_fps)]
# Uniformly sample frames if the number of frames is too large
if len(frame_idx) > MAX_NUM_FRAMES:
frame_idx = uniform_sample(frame_idx, MAX_NUM_FRAMES)
# Extract frames from the video
frames = vr.get_batch(frame_idx).asnumpy()
frames = [Image.fromarray(v.astype("uint8")) for v in frames]
# Return video frames
return frames
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