Spaces:
Running
on
Zero
Running
on
Zero
Update app.py
Browse files
app.py
CHANGED
@@ -12,7 +12,6 @@ import numpy as np
|
|
12 |
from PIL import Image
|
13 |
import spaces
|
14 |
|
15 |
-
|
16 |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
17 |
processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
|
18 |
model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble").to(device)
|
@@ -40,8 +39,7 @@ def annotate_image(
|
|
40 |
output_image = LABEL_ANNOTATOR.annotate(output_image, detections, labels=labels)
|
41 |
return output_image
|
42 |
|
43 |
-
|
44 |
-
|
45 |
def process_video(
|
46 |
input_video,
|
47 |
labels,
|
@@ -63,7 +61,6 @@ def process_video(
|
|
63 |
# list of dict of {"box": box, "mask":mask, "score":score, "label":label}
|
64 |
results = query(frame, labels)
|
65 |
|
66 |
-
#detections = sv.Detections.empty()
|
67 |
detections = sv.Detections.from_transformers(results[0])
|
68 |
final_labels = []
|
69 |
for id in results[0]["labels"]:
|
@@ -76,7 +73,6 @@ def process_video(
|
|
76 |
sink.write_frame(frame)
|
77 |
return result_file_path
|
78 |
|
79 |
-
@spaces.GPU
|
80 |
def query(image, texts):
|
81 |
inputs = processor(text=texts, images=image, return_tensors="pt").to(device)
|
82 |
with torch.no_grad():
|
|
|
12 |
from PIL import Image
|
13 |
import spaces
|
14 |
|
|
|
15 |
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
16 |
processor = Owlv2Processor.from_pretrained("google/owlv2-base-patch16-ensemble")
|
17 |
model = Owlv2ForObjectDetection.from_pretrained("google/owlv2-base-patch16-ensemble").to(device)
|
|
|
39 |
output_image = LABEL_ANNOTATOR.annotate(output_image, detections, labels=labels)
|
40 |
return output_image
|
41 |
|
42 |
+
@spaces.GPU
|
|
|
43 |
def process_video(
|
44 |
input_video,
|
45 |
labels,
|
|
|
61 |
# list of dict of {"box": box, "mask":mask, "score":score, "label":label}
|
62 |
results = query(frame, labels)
|
63 |
|
|
|
64 |
detections = sv.Detections.from_transformers(results[0])
|
65 |
final_labels = []
|
66 |
for id in results[0]["labels"]:
|
|
|
73 |
sink.write_frame(frame)
|
74 |
return result_file_path
|
75 |
|
|
|
76 |
def query(image, texts):
|
77 |
inputs = processor(text=texts, images=image, return_tensors="pt").to(device)
|
78 |
with torch.no_grad():
|