File size: 5,119 Bytes
52a058d 53b7033 52a058d 53b7033 dd8e993 52a058d e576e22 cce6990 52a058d |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 |
import cv2
import gradio as gr
import imutils
import numpy as np
import torch
from pytorchvideo.transforms import (
ApplyTransformToKey,
Normalize,
RandomShortSideScale,
RemoveKey,
ShortSideScale,
UniformTemporalSubsample,
)
from torchvision.transforms import (
Compose,
Lambda,
RandomCrop,
RandomHorizontalFlip,
Resize,
)
from transformers import VideoMAEFeatureExtractor, VideoMAEForVideoClassification
MODEL_CKPT = "archit11/videomae-base-finetuned-ucfcrime-full"
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
MODEL = VideoMAEForVideoClassification.from_pretrained(MODEL_CKPT).to(DEVICE)
PROCESSOR = VideoMAEFeatureExtractor.from_pretrained(MODEL_CKPT)
RESIZE_TO = PROCESSOR.size["shortest_edge"]
NUM_FRAMES_TO_SAMPLE = MODEL.config.num_frames
IMAGE_STATS = {"image_mean": [0.485, 0.456, 0.406], "image_std": [0.229, 0.224, 0.225]}
VAL_TRANSFORMS = Compose(
[
UniformTemporalSubsample(NUM_FRAMES_TO_SAMPLE),
Lambda(lambda x: x / 255.0),
Normalize(IMAGE_STATS["image_mean"], IMAGE_STATS["image_std"]),
Resize((RESIZE_TO, RESIZE_TO)),
]
)
LABELS = list(MODEL.config.label2id.keys())
def parse_video(video_file):
"""A utility to parse the input videos.
Reference: https://pyimagesearch.com/2018/11/12/yolo-object-detection-with-opencv/
"""
vs = cv2.VideoCapture(video_file)
# try to determine the total number of frames in the video file
try:
prop = (
cv2.cv.CV_CAP_PROP_FRAME_COUNT
if imutils.is_cv2()
else cv2.CAP_PROP_FRAME_COUNT
)
total = int(vs.get(prop))
print("[INFO] {} total frames in video".format(total))
# an error occurred while trying to determine the total
# number of frames in the video file
except:
print("[INFO] could not determine # of frames in video")
print("[INFO] no approx. completion time can be provided")
total = -1
frames = []
# loop over frames from the video file stream
while True:
# read the next frame from the file
(grabbed, frame) = vs.read()
if frame is not None:
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
frames.append(frame)
# if the frame was not grabbed, then we have reached the end
# of the stream
if not grabbed:
break
return frames
def preprocess_video(frames: list):
"""Utility to apply preprocessing transformations to a video tensor."""
# Each frame in the `frames` list has the shape: (height, width, num_channels).
# Collated together the `frames` has the the shape: (num_frames, height, width, num_channels).
# So, after converting the `frames` list to a torch tensor, we permute the shape
# such that it becomes (num_channels, num_frames, height, width) to make
# the shape compatible with the preprocessing transformations. After applying the
# preprocessing chain, we permute the shape to (num_frames, num_channels, height, width)
# to make it compatible with the model. Finally, we add a batch dimension so that our video
# classification model can operate on it.
video_tensor = torch.tensor(np.array(frames).astype(frames[0].dtype))
video_tensor = video_tensor.permute(
3, 0, 1, 2
) # (num_channels, num_frames, height, width)
video_tensor_pp = VAL_TRANSFORMS(video_tensor)
video_tensor_pp = video_tensor_pp.permute(
1, 0, 2, 3
) # (num_frames, num_channels, height, width)
video_tensor_pp = video_tensor_pp.unsqueeze(0)
return video_tensor_pp.to(DEVICE)
def infer(video_file):
frames = parse_video(video_file)
video_tensor = preprocess_video(frames)
inputs = {"pixel_values": video_tensor}
# forward pass
with torch.no_grad():
outputs = MODEL(**inputs)
logits = outputs.logits
softmax_scores = torch.nn.functional.softmax(logits, dim=-1).squeeze(0)
confidences = {LABELS[i]: float(softmax_scores[i]) for i in range(len(LABELS))}
return confidences
gr.Interface(
fn=infer,
inputs=gr.Video(),
outputs=gr.Label(num_top_classes=13),
examples=[
["examples/fight.mp4"],
["examples/baseball.mp4"],
["examples/balancebeam.mp4"],
["./examples/no-fight1.mp4"],
["./examples/no-fight2.mp4"],
["./examples/no-fight3.mp4"],
["./examples/no-fight4.mp4"],
],
title="VideoMAE fin-tuned on a subset of Fight / No Fight dataset",
description=(
"Gradio demo for VideoMAE for video classification. To use it, simply upload your video or click one of the"
" examples to load them. Read more at the links below."
),
article=(
"<div style='text-align: center;'><a href='https://huggingface.co/docs/transformers/model_doc/videomae' target='_blank'>VideoMAE</a>"
" <center><a href='https://huggingface.co/sayakpaul/videomae-base-finetuned-kinetics-finetuned-ucf101-subset' target='_blank'>Fine-tuned Model</a></center></div>"
),
allow_flagging=False,
).launch() |