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import subprocess
import sys
from os.path import abspath, dirname,join
sys.path.append(join(dirname(abspath(__file__)),'GroundingDINO'))
def run_commands():
commands = [
"git clone https://github.com/IDEA-Research/GroundingDINO.git",
"pip install -e ./GroundingDINO",
"cd GroundingDINO",
"mkdir GroundingDINO/weights",
"cd GroundingDINO/weights && wget -q https://github.com/IDEA-Research/GroundingDINO/releases/download/v0.1.0-alpha/groundingdino_swint_ogc.pth",
"ls"
]
for command in commands:
try:
print(f"Running command: {command}")
result = subprocess.run(command, shell=True, check=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
print(result.stdout.decode())
except subprocess.CalledProcessError as e:
print(f"Command '{command}' failed with error: {e.stderr.decode()}")
@spaces.GPU(30)
def video_app_setup_and_run_pipeline():
run_commands()
class DinoVisionTransformerClassifier(nn.Module):
def __init__(self):
super(DinoVisionTransformerClassifier, self).__init__()
self.transformer = torch.hub.load("facebookresearch/dinov2", "dinov2_vits14")
self.classifier = nn.Sequential(nn.Linear(384, 256), nn.ReLU(), nn.Linear(256, 2))
def forward(self, x):
x = self.transformer(x)
x = self.transformer.norm(x)
x = self.classifier(x)
return x
class ImageClassifier:
def __init__(self):
with open(f"{dirname(abspath(__file__))}/config.yaml", 'r') as f:
config = yaml.load(f, Loader=yaml.FullLoader)
labels = config["labels"]
self.labels = labels
self.dino = DinoVisionTransformerClassifier()
model_path = f"{dirname(abspath(__file__))}/model.pth"
state_dict = torch.load(model_path)
self.dino.load_state_dict(state_dict)
def preprocess(self, image: np.ndarray) -> torch.Tensor:
data_transforms = {
"test": transforms.Compose(
[
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),
]
)
}
image_pillow = Image.fromarray(image)
img_transformed = data_transforms['test'](image_pillow)
return img_transformed
def predict(self, image):
image = self.preprocess(image)
image = image.unsqueeze(0)
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
self.dino.to(device)
self.dino.eval()
with torch.no_grad():
output = self.dino(image.to(device))
logit, predicted = torch.max(output.data, 1)
return self.labels[predicted[0].item()], logit[0].item()
class VideoObjectDetection:
def __init__(self,
text_prompt: str
):
self.text_prompt = text_prompt
def crop(self, frame, boxes):
h, w, _ = frame.shape
boxes = boxes * torch.Tensor([w, h, w, h])
xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
min_col, min_row, max_col, max_row = map(int, xyxy[0])
crop_image = frame[min_row:max_row, min_col:max_col, :]
return crop_image
def annotate(self,
image_source: np.ndarray,
boxes: torch.Tensor,
logits: torch.Tensor,
phrases: List[str],
frame_rgb: np.ndarray,
classifier) -> np.ndarray:
h, w, _ = image_source.shape
boxes = boxes * torch.Tensor([w, h, w, h])
xyxy = box_convert(boxes=boxes, in_fmt="cxcywh", out_fmt="xyxy").numpy()
detections = sv.Detections(xyxy=xyxy)
print(xyxy.shape)
custom_labels = []
custom_logits = []
for box in xyxy:
min_col, min_row, max_col, max_row = map(int, box)
crop_image = frame_rgb[min_row:max_row, min_col:max_col, :]
label, logit = classifier.predict(crop_image)
print()
if logit >= 1:
custom_labels.append(label)
custom_logits.append(logit)
else:
custom_labels.append('unknown human face')
custom_logits.append(logit)
labels = [
f"{phrase} {logit:.2f}"
for phrase, logit
in zip(custom_labels, custom_logits)
]
box_annotator = sv.BoxAnnotator()
annotated_frame = box_annotator.annotate(scene=image_source, detections=detections, labels=labels)
return annotated_frame
def preprocess_image(self, image: np.ndarray) -> torch.Tensor:
transform = T.Compose(
[
T.RandomResize([800], max_size=1333),
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]
)
image_pillow = Image.fromarray(image)
image_transformed, _ = transform(image_pillow, None)
return image_transformed
def generate_video(self, video_path) -> None:
# Load model, set up variables and get video properties
cap, fps, width, height, fourcc = get_video_properties(video_path)
model = load_model("GroundingDINO/groundingdino/config/GroundingDINO_SwinT_OGC.py",
"GroundingDINO/weights/groundingdino_swint_ogc.pth")
predictor = ImageClassifier()
TEXT_PROMPT = self.text_prompt
BOX_TRESHOLD = 0.6
TEXT_TRESHOLD = 0.6
# Read video frames, crop image based on text prompt object detection and generate dataset_train
import time
frame_count = 0
delay = 1 / fps # Delay in seconds between frames
while cap.isOpened():
start_time = time.time()
ret, frame = cap.read()
if not ret:
break
if cv2.waitKey(1) & 0xff == ord('q'):
break
# Convert bgr frame to rgb frame to image to torch tensor transformed
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
image_transformed = self.preprocess_image(frame_rgb)
boxes, logits, phrases = predict(
model=model,
image=image_transformed,
caption=TEXT_PROMPT,
box_threshold=BOX_TRESHOLD,
text_threshold=TEXT_TRESHOLD
)
# Get boxes
if boxes.size()[0] > 0:
annotated_frame = self.annotate(image_source=frame, boxes=boxes, logits=logits,
phrases=phrases, frame_rgb=frame_rgb, classifier=predictor)
# cv2.imshow('Object detection', annotated_frame)
frame_rgb = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB)
yield frame_rgb
elapsed_time = time.time() - start_time
time_to_wait = max(delay - elapsed_time, 0)
time.sleep(time_to_wait)
frame_count += 1
from typing import List
from Utils import get_video_properties
from GroundingDINO.groundingdino.util.inference import load_model, predict
import cv2
import numpy as np
import torch
from PIL import Image
import GroundingDINO.groundingdino.datasets.transforms as T
from torchvision.ops import box_convert
from torchvision import transforms
from torch import nn
from os.path import dirname, abspath
import yaml
import supervision as sv
import gradio as gr
import spaces
video_annotator = VideoObjectDetection(
text_prompt='human face')
with gr.Blocks() as iface:
video_input = gr.Video(label="Upload Video")
run_button = gr.Button("Start Processing")
output_image = gr.Image(label="Classified video")
run_button.click(fn=video_annotator.generate_video, inputs=video_input,
outputs=output_image)
iface.launch(share=False, debug=True)
if __name__ == "__main__":
video_app_setup_and_run_pipeline()
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