Spaces:
Build error
Build error
Update app.py
Browse files
app.py
CHANGED
@@ -1,44 +1,53 @@
|
|
1 |
import gradio as gr
|
2 |
-
|
3 |
from ultralytics import YOLO
|
4 |
-
from
|
5 |
-
from PIL import Image, ImageDraw
|
6 |
|
7 |
-
|
8 |
-
model_path = hf_hub_download(
|
9 |
-
repo_id="arnabdhar/YOLOv8-Face-Detection",
|
10 |
-
filename="model.pt"
|
11 |
-
)
|
12 |
-
model = YOLO(model_path)
|
13 |
-
|
14 |
-
def count_faces(image: Image.Image):
|
15 |
"""
|
16 |
-
|
|
|
17 |
"""
|
18 |
-
|
19 |
-
|
20 |
-
|
|
|
|
|
21 |
|
22 |
-
|
23 |
-
|
24 |
-
|
25 |
-
|
26 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
27 |
|
28 |
-
#
|
29 |
-
|
|
|
30 |
|
31 |
-
# Gradio interface
|
32 |
-
|
33 |
-
fn=
|
34 |
inputs=gr.Image(type="pil", label="Upload Image"),
|
35 |
-
outputs=
|
36 |
-
|
37 |
-
|
38 |
-
|
39 |
-
|
40 |
-
|
|
|
|
|
|
|
|
|
41 |
)
|
42 |
|
43 |
if __name__ == "__main__":
|
44 |
-
|
|
|
1 |
import gradio as gr
|
2 |
+
import torch
|
3 |
from ultralytics import YOLO
|
4 |
+
from PIL import Image
|
|
|
5 |
|
6 |
+
def load_model():
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
7 |
"""
|
8 |
+
Load the YOLOv8 segmentation model onto GPU (if available)
|
9 |
+
with mixed‑precision enabled.
|
10 |
"""
|
11 |
+
device = "cuda" if torch.cuda.is_available() else "cpu" # GPU if available :contentReference[oaicite:2]{index=2}
|
12 |
+
model = YOLO('yolov8x-seg.pt').to(device) # Segmentation variant for finer masks :contentReference[oaicite:3]{index=3}
|
13 |
+
return model, device
|
14 |
+
|
15 |
+
model, device = load_model()
|
16 |
|
17 |
+
def count_persons(image: Image.Image) -> str:
|
18 |
+
"""
|
19 |
+
Run inference on the input image, apply TTA, filter for class 0 (person),
|
20 |
+
and return the total count.
|
21 |
+
"""
|
22 |
+
# Perform prediction with augmentation (TTA), limit detections, and only class 0
|
23 |
+
results = model.predict(
|
24 |
+
source=image,
|
25 |
+
conf=0.6, # Confidence threshold
|
26 |
+
imgsz=640, # Inference resolution
|
27 |
+
augment=True, # Test Time Augmentation :contentReference[oaicite:4]{index=4}
|
28 |
+
max_det=300, # Cap detections for crowded scenes
|
29 |
+
classes=[0] # Only detect persons (class 0) :contentReference[oaicite:5]{index=5}
|
30 |
+
)
|
31 |
|
32 |
+
# Sum counts across all results (usually one per image)
|
33 |
+
total = sum(len(r.boxes) for r in results)
|
34 |
+
return f"Persons detected: {total}"
|
35 |
|
36 |
+
# Build Gradio interface
|
37 |
+
demo = gr.Interface(
|
38 |
+
fn=count_persons,
|
39 |
inputs=gr.Image(type="pil", label="Upload Image"),
|
40 |
+
outputs=gr.Text(label="Person Count"),
|
41 |
+
title="Advanced Person Counter with YOLOv8",
|
42 |
+
description=(
|
43 |
+
"Upload an image to count people using a state‑of‑the‑art "
|
44 |
+
"YOLOv8 segmentation model with Test‑Time Augmentation."
|
45 |
+
),
|
46 |
+
examples=[ # optional: add example images if you like
|
47 |
+
# ["examples/crowd1.jpg"],
|
48 |
+
# ["examples/street_scene.jpg"],
|
49 |
+
]
|
50 |
)
|
51 |
|
52 |
if __name__ == "__main__":
|
53 |
+
demo.launch() # Launch locally; add `share=True` for a public link
|