Spaces:
Running
on
Zero
Running
on
Zero
File size: 3,636 Bytes
8e604f8 0350aec 98ea781 0350aec |
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 |
# type: ignore
import spaces
import gradio as gr
import cv2
from PIL import Image
import torch
import time
import numpy as np
import uuid
from transformers import RTDetrForObjectDetection, RTDetrImageProcessor # type: ignore
from draw_boxes import draw_bounding_boxes
image_processor = RTDetrImageProcessor.from_pretrained("PekingU/rtdetr_r50vd")
model = RTDetrForObjectDetection.from_pretrained("PekingU/rtdetr_r50vd").to("cuda")
SUBSAMPLE = 2
@spaces.GPU
def stream_object_detection(video, conf_threshold):
cap = cv2.VideoCapture(video)
video_codec = cv2.VideoWriter_fourcc(*"mp4v") # type: ignore
fps = int(cap.get(cv2.CAP_PROP_FPS))
desired_fps = fps // SUBSAMPLE
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) // 2
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) // 2
iterating, frame = cap.read()
n_frames = 0
name = f"output_{uuid.uuid4()}.mp4"
segment_file = cv2.VideoWriter(name, video_codec, desired_fps, (width, height)) # type: ignore
batch = []
while iterating:
frame = cv2.resize(frame, (0, 0), fx=0.5, fy=0.5)
frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
if n_frames % SUBSAMPLE == 0:
batch.append(frame)
if len(batch) == 2 * desired_fps:
inputs = image_processor(images=batch, return_tensors="pt").to("cuda")
print(f"starting batch of size {len(batch)}")
start = time.time()
with torch.no_grad():
outputs = model(**inputs)
end = time.time()
print("time taken for inference", end - start)
start = time.time()
boxes = image_processor.post_process_object_detection(
outputs,
target_sizes=torch.tensor([(height, width)] * len(batch)),
threshold=conf_threshold,
)
for _, (array, box) in enumerate(zip(batch, boxes)):
pil_image = draw_bounding_boxes(
Image.fromarray(array), box, model, conf_threshold
)
frame = np.array(pil_image)
# Convert RGB to BGR
frame = frame[:, :, ::-1].copy()
segment_file.write(frame)
batch = []
segment_file.release()
yield name
end = time.time()
print("time taken for processing boxes", end - start)
name = f"output_{uuid.uuid4()}.mp4"
segment_file = cv2.VideoWriter(
name, video_codec, desired_fps, (width, height)
) # type: ignore
iterating, frame = cap.read()
n_frames += 1
with gr.Blocks() as demo:
gr.HTML(
"""
<h1 style='text-align: center'>
Video Object Detection with <a href='https://huggingface.co/PekingU/rtdetr_r101vd_coco_o365' target='_blank'>RT-DETR</a>
</h1>
"""
)
with gr.Row():
with gr.Column():
video = gr.Video(label="Video Source")
conf_threshold = gr.Slider(
label="Confidence Threshold",
minimum=0.0,
maximum=1.0,
step=0.05,
value=0.30,
)
with gr.Column():
output_video = gr.Video(
label="Processed Video", streaming=True, autoplay=True
)
video.upload(
fn=stream_object_detection,
inputs=[video, conf_threshold],
outputs=[output_video],
)
gr.Examples(
examples=["3285790-hd_1920_1080_30fps.mp4"],
inputs=[video],
)
if __name__ == "__main__":
demo.launch()
|