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from pathlib import Path
from urllib.parse import urlparse
import numpy as np
from PIL import Image
def get_video_frames(video_pth, frames_video=15):
import cv2
assert Path(video_pth).exists(), f"Video {video_pth} does not exist"
video_pth = str(video_pth)
# use OpenCV to read the video
cap = cv2.VideoCapture(video_pth)
# get the total number of frames in the video
total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
frame_idxs = sample_frames(total_frames, n_frames=frames_video)
frames = []
f_idxs = []
for frame_idx in frame_idxs:
cap.set(cv2.CAP_PROP_POS_FRAMES, frame_idx)
ret, frame = cap.read()
if not ret or frame is None:
print(f"Video {video_pth} is corrupted")
frames = [
Image.fromarray(np.zeros((384, 384, 3)).astype(np.uint8))
] * frames_video
f_idxs = [-1] * frames_video
return frames, f_idxs
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(Image.fromarray(frame))
f_idxs.append(frame_idx)
# pad frames to have the same number of frames
n_frames = len(frames)
if n_frames < frames_video:
frames += [Image.fromarray(np.zeros((384, 384, 3)).astype(np.uint8))] * (
frames_video - n_frames
)
# Add -1 to f_idxs for the remaining frames
f_idxs += [-1] * (frames_video - len(f_idxs))
return frames, f_idxs
def sample_frames(vlen, n_frames=15):
acc_samples = min(vlen, n_frames)
intervals = np.linspace(start=0, stop=vlen, num=acc_samples + 1).astype(int)
ranges = []
for idx, interv in enumerate(intervals[:-1]):
ranges.append((interv, intervals[idx + 1] - 1))
frame_idxs = [(x[0] + x[1]) // 2 for x in ranges]
return frame_idxs
def concat_h_imgs(im_list, resample=Image.Resampling.BICUBIC):
min_height = min(im.height for im in im_list)
im_list_resize = [
im.resize(
(int(im.width * min_height / im.height), min_height), resample=resample
)
for im in im_list
]
total_width = sum(im.width for im in im_list_resize)
dst = Image.new("RGB", (total_width, min_height))
pos_x = 0
for im in im_list_resize:
dst.paste(im, (pos_x, 0))
pos_x += im.width
return dst
def extract_frames(url, n_frames=10):
import urllib.request
import cv2
import numpy as np
from PIL import Image
# Download the video from the URL
resp = urllib.request.urlopen(url)
video = resp.read()
# Load the video using OpenCV
video = cv2.VideoCapture(url)
# Get the total number of frames in the video
total_frames = int(video.get(cv2.CAP_PROP_FRAME_COUNT))
# Sample `n_frames` frames from the video
frames_idxs = sample_frames(total_frames, n_frames=n_frames)
frames = []
for frame_number in frames_idxs:
video.set(cv2.CAP_PROP_POS_FRAMES, frame_number)
ret, frame = video.read()
if ret:
# Convert the color channels from BGR to RGB
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
# Convert the NumPy array to a PIL Image object
pil_image = Image.fromarray(np.uint8(frame))
frames.append(pil_image)
return frames
def visualize_url_video(url, n_frames=10):
frames = extract_frames(url, n_frames=n_frames)
if n_frames == 1:
return frames[0]
return concat_h_imgs(frames)
def visualize_pth_video(video_pth, n_frames=10):
frames, _ = get_video_frames(video_pth, frames_video=n_frames)
if n_frames == 1:
return frames[0]
return concat_h_imgs(frames)
def visualize_video(video, n_frames=10):
if is_url(video):
return visualize_url_video(video, n_frames=n_frames)
return visualize_pth_video(video, n_frames=n_frames)
def is_url(url_or_filename):
parsed = urlparse(str(url_or_filename))
return parsed.scheme in ("http", "https")
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