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import gradio as gr
import torch
import numpy as np
from transformers import AutoProcessor, AutoModel
from PIL import Image
import cv2
from pathlib import Path
from tempfile import NamedTemporaryFile
MODEL_NAME = "microsoft/xclip-base-patch16-zero-shot"
CLIP_LEN = 32
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
processor = AutoProcessor.from_pretrained(MODEL_NAME)
model = AutoModel.from_pretrained(MODEL_NAME).to(device)
def get_video_length(file_path):
cap = cv2.VideoCapture(file_path)
length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
cap.release()
return length
def read_video_opencv(file_path, indices):
frames = []
failed_indices = []
cap = cv2.VideoCapture(file_path)
if not cap.isOpened():
print(f"Error opening video file: {file_path}")
return frames
max_index = int(cap.get(cv2.CAP_PROP_FRAME_COUNT)) - 1
for idx in indices:
if idx <= max_index:
frame = get_frame_with_opened_cap(cap, idx)
if frame is not None:
frames.append(frame)
else:
failed_indices.append(idx)
else:
failed_indices.append(idx)
cap.release()
if failed_indices:
print(f"Failed to extract frames at indices: {failed_indices}")
return frames
def get_frame_with_opened_cap(cap, index):
cap.set(cv2.CAP_PROP_POS_FRAMES, index)
ret, frame = cap.read()
if ret:
return cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
return None
def sample_uniform_frame_indices(clip_len, seg_len):
if seg_len < clip_len:
repeat_factor = np.ceil(clip_len / seg_len).astype(int)
indices = np.arange(seg_len).tolist() * repeat_factor
indices = indices[:clip_len]
else:
spacing = seg_len // clip_len
indices = [i * spacing for i in range(clip_len)]
return np.array(indices).astype(np.int64)
def concatenate_frames(frames, clip_len):
layout = { 32: (4, 8) }
rows, cols = layout[clip_len]
combined_image = Image.new('RGB', (frames[0].shape[1]*cols, frames[0].shape[0]*rows))
frame_iter = iter(frames)
y_offset = 0
for i in range(rows):
x_offset = 0
for j in range(cols):
img = Image.fromarray(next(frame_iter))
combined_image.paste(img, (x_offset, y_offset))
x_offset += frames[0].shape[1]
y_offset += frames[0].shape[0]
return combined_image
def model_interface(uploaded_video, activity):
video_length = get_video_length(uploaded_video)
indices = sample_uniform_frame_indices(CLIP_LEN, seg_len=video_length)
video = read_video_opencv(uploaded_video, indices)
concatenated_image = concatenate_frames(video, CLIP_LEN)
activities_list = [activity, "other"]
inputs = processor(
text=activities_list,
videos=list(video),
return_tensors="pt",
padding=True,
)
for key, value in inputs.items():
if isinstance(value, torch.Tensor):
inputs[key] = value.to(device)
with torch.no_grad():
outputs = model(**inputs)
logits_per_video = outputs.logits_per_video
probs = logits_per_video.softmax(dim=1)
results_probs = []
results_logits = []
max_prob_index = torch.argmax(probs[0]).item()
for i in range(len(activities_list)):
current_activity = activities_list[i]
prob = float(probs[0][i].cpu())
logit = float(logits_per_video[0][i].cpu())
results_probs.append((current_activity, f"Probability: {prob * 100:.2f}%"))
results_logits.append((current_activity, f"Raw Score: {logit:.2f}"))
likely_label = activities_list[max_prob_index]
likely_probability = float(probs[0][max_prob_index].cpu()) * 100
return concatenated_image, results_probs, results_logits, [likely_label, likely_probability]
iface = gr.Interface(
fn=model_interface,
inputs=[
gr.Video(label="Upload a Video"),
gr.Textbox(label="Activity to Detect")
],
outputs=[
gr.Image(label="Concatenated Frames"),
gr.Dataframe(headers=["Activity", "Probability"], label="Probabilities"),
gr.Dataframe(headers=["Activity", "Raw Score"], label="Raw Scores"),
gr.Textbox(label="Most Likely Activity")
],
title="Video Activity Classifier",
description="""
**Instructions:**
1. **Upload a Video**: Select a video file to upload.
2. **Enter Activity Label**: Specify the activity you want to detect in the video.
3. **View Results**:
- The concatenated frames from the video will be displayed.
- Probabilities and raw scores for the specified activity and the "other" category will be shown.
- The most likely activity detected in the video will be displayed.
"""
)
if __name__ == "__main__":
iface.launch()
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