|
import subprocess |
|
import sys |
|
from os.path import abspath, dirname,join |
|
import spaces |
|
|
|
|
|
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()}") |
|
|
|
|
|
from typing import List |
|
from Utils import get_video_properties |
|
import cv2 |
|
import numpy as np |
|
import torch |
|
from PIL import Image |
|
from torchvision.ops import box_convert |
|
from torchvision import transforms |
|
from torch import nn |
|
import yaml |
|
import supervision as sv |
|
import gradio as gr |
|
|
|
|
|
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() |
|
|
|
def pipeline_to_setup_with_gpu(): |
|
run_commands() |
|
sys.path.append(join(dirname(abspath(__file__)),'GroundingDINO')) |
|
from GroundingDINO.groundingdino.util.inference import load_model, predict |
|
import GroundingDINO.groundingdino.datasets.transforms as T |
|
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: |
|
|
|
|
|
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 |
|
|
|
|
|
import time |
|
frame_count = 0 |
|
delay = 1 / fps |
|
while cap.isOpened(): |
|
start_time = time.time() |
|
ret, frame = cap.read() |
|
if not ret: |
|
break |
|
|
|
if cv2.waitKey(1) & 0xff == ord('q'): |
|
break |
|
|
|
|
|
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 |
|
) |
|
|
|
|
|
if boxes.size()[0] > 0: |
|
annotated_frame = self.annotate(image_source=frame, boxes=boxes, logits=logits, |
|
phrases=phrases, frame_rgb=frame_rgb, classifier=predictor) |
|
|
|
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 |
|
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__": |
|
pipeline_to_setup_with_gpu() |
|
|
|
|
