Spaces:
Sleeping
Sleeping
SerdarHelli
commited on
Commit
•
ce7d026
1
Parent(s):
297a2c6
Update app.py
Browse files
app.py
CHANGED
@@ -5,37 +5,25 @@ import sahi.predict
|
|
5 |
import sahi.slicing
|
6 |
from PIL import Image
|
7 |
import numpy
|
|
|
|
|
|
|
8 |
|
9 |
IMAGE_SIZE = 640
|
10 |
|
11 |
-
|
12 |
-
sahi.utils.file.download_from_url(
|
13 |
-
"https://user-images.githubusercontent.com/34196005/142730935-2ace3999-a47b-49bb-83e0-2bdd509f1c90.jpg",
|
14 |
-
"apple_tree.jpg",
|
15 |
-
)
|
16 |
-
sahi.utils.file.download_from_url(
|
17 |
-
"https://user-images.githubusercontent.com/34196005/142730936-1b397756-52e5-43be-a949-42ec0134d5d8.jpg",
|
18 |
-
"highway.jpg",
|
19 |
-
)
|
20 |
|
21 |
-
sahi.utils.file.download_from_url(
|
22 |
-
"https://user-images.githubusercontent.com/34196005/142742871-bf485f84-0355-43a3-be86-96b44e63c3a2.jpg",
|
23 |
-
"highway2.jpg",
|
24 |
-
)
|
25 |
-
|
26 |
-
sahi.utils.file.download_from_url(
|
27 |
-
"https://user-images.githubusercontent.com/34196005/142742872-1fefcc4d-d7e6-4c43-bbb7-6b5982f7e4ba.jpg",
|
28 |
-
"highway3.jpg",
|
29 |
-
)
|
30 |
|
|
|
31 |
|
32 |
# Model
|
33 |
model = AutoDetectionModel.from_pretrained(
|
34 |
-
model_type="yolov5", model_path=
|
35 |
)
|
36 |
|
37 |
|
38 |
def sahi_yolo_inference(
|
|
|
39 |
image,
|
40 |
slice_height=512,
|
41 |
slice_width=512,
|
@@ -47,34 +35,26 @@ def sahi_yolo_inference(
|
|
47 |
postprocess_class_agnostic=False,
|
48 |
):
|
49 |
|
50 |
-
image_width, image_height = image.size
|
51 |
-
sliced_bboxes = sahi.slicing.get_slice_bboxes(
|
52 |
-
|
53 |
-
|
54 |
-
|
55 |
-
|
56 |
-
|
57 |
-
|
58 |
-
|
59 |
-
)
|
60 |
-
if len(sliced_bboxes) > 60:
|
61 |
-
|
62 |
-
|
63 |
-
|
64 |
-
|
65 |
-
|
66 |
-
|
67 |
-
|
68 |
-
|
69 |
-
|
70 |
-
visual_result_1 = sahi.utils.cv.visualize_object_predictions(
|
71 |
-
image=numpy.array(image),
|
72 |
-
object_prediction_list=prediction_result_1.object_prediction_list,
|
73 |
-
)
|
74 |
-
output_1 = Image.fromarray(visual_result_1["image"])
|
75 |
-
|
76 |
-
# sliced inference
|
77 |
-
prediction_result_2 = sahi.predict.get_sliced_prediction(
|
78 |
image=image,
|
79 |
detection_model=model,
|
80 |
slice_height=int(slice_height),
|
@@ -85,18 +65,33 @@ def sahi_yolo_inference(
|
|
85 |
postprocess_match_metric=postprocess_match_metric,
|
86 |
postprocess_match_threshold=postprocess_match_threshold,
|
87 |
postprocess_class_agnostic=postprocess_class_agnostic,
|
88 |
-
|
89 |
-
|
90 |
-
|
91 |
-
|
92 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
93 |
|
94 |
-
output_2 = Image.fromarray(visual_result_2["image"])
|
95 |
|
96 |
-
return
|
97 |
|
98 |
|
99 |
inputs = [
|
|
|
100 |
gr.inputs.Image(type="pil", label="Original Image"),
|
101 |
gr.inputs.Number(default=512, label="slice_height"),
|
102 |
gr.inputs.Number(default=512, label="slice_width"),
|
@@ -116,8 +111,7 @@ inputs = [
|
|
116 |
]
|
117 |
|
118 |
outputs = [
|
119 |
-
gr.outputs.Image(type="pil", label="
|
120 |
-
gr.outputs.Image(type="pil", label="YOLOv5s + SAHI"),
|
121 |
]
|
122 |
|
123 |
title = "Small Object Detection with SAHI + YOLOv5"
|
|
|
5 |
import sahi.slicing
|
6 |
from PIL import Image
|
7 |
import numpy
|
8 |
+
from huggingface_hub import hf_hub_download
|
9 |
+
import torch
|
10 |
+
|
11 |
|
12 |
IMAGE_SIZE = 640
|
13 |
|
14 |
+
model_path=hf_hub_download("kadirnar/deprem_model_v1", filename="last.pt",revision="main")
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
15 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
16 |
|
17 |
+
current_device='cuda' if torch.cuda.is_available() else 'cpu'
|
18 |
|
19 |
# Model
|
20 |
model = AutoDetectionModel.from_pretrained(
|
21 |
+
model_type="yolov5", model_path=model_path, device=current_device, confidence_threshold=0.5, image_size=IMAGE_SIZE
|
22 |
)
|
23 |
|
24 |
|
25 |
def sahi_yolo_inference(
|
26 |
+
model_type,
|
27 |
image,
|
28 |
slice_height=512,
|
29 |
slice_width=512,
|
|
|
35 |
postprocess_class_agnostic=False,
|
36 |
):
|
37 |
|
38 |
+
#image_width, image_height = image.size
|
39 |
+
# sliced_bboxes = sahi.slicing.get_slice_bboxes(
|
40 |
+
# image_height,
|
41 |
+
# image_width,
|
42 |
+
# slice_height,
|
43 |
+
# slice_width,
|
44 |
+
# False,
|
45 |
+
# overlap_height_ratio,
|
46 |
+
# overlap_width_ratio,
|
47 |
+
# )
|
48 |
+
# if len(sliced_bboxes) > 60:
|
49 |
+
# raise ValueError(
|
50 |
+
# f"{len(sliced_bboxes)} slices are too much for huggingface spaces, try smaller slice size."
|
51 |
+
# )
|
52 |
+
|
53 |
+
|
54 |
+
|
55 |
+
|
56 |
+
if "SAHI" in model_type:
|
57 |
+
prediction_result_2 = sahi.predict.get_sliced_prediction(
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
58 |
image=image,
|
59 |
detection_model=model,
|
60 |
slice_height=int(slice_height),
|
|
|
65 |
postprocess_match_metric=postprocess_match_metric,
|
66 |
postprocess_match_threshold=postprocess_match_threshold,
|
67 |
postprocess_class_agnostic=postprocess_class_agnostic,
|
68 |
+
)
|
69 |
+
visual_result_2 = sahi.utils.cv.visualize_object_predictions(
|
70 |
+
image=numpy.array(image),
|
71 |
+
object_prediction_list=prediction_result_2.object_prediction_list,
|
72 |
+
)
|
73 |
+
output = Image.fromarray(visual_result_2["image"])
|
74 |
+
|
75 |
+
else:
|
76 |
+
# standard inference
|
77 |
+
prediction_result_1 = sahi.predict.get_prediction(
|
78 |
+
image=image, detection_model=model
|
79 |
+
)
|
80 |
+
print(image)
|
81 |
+
visual_result_1 = sahi.utils.cv.visualize_object_predictions(
|
82 |
+
image=numpy.array(image),
|
83 |
+
object_prediction_list=prediction_result_1.object_prediction_list,
|
84 |
+
)
|
85 |
+
output = Image.fromarray(visual_result_1["image"])
|
86 |
+
|
87 |
+
# sliced inference
|
88 |
|
|
|
89 |
|
90 |
+
return output
|
91 |
|
92 |
|
93 |
inputs = [
|
94 |
+
gr.Dropdown(choices=["YOLOv5","YOLOv5 + SAHI"],label="Choose Model Type"),
|
95 |
gr.inputs.Image(type="pil", label="Original Image"),
|
96 |
gr.inputs.Number(default=512, label="slice_height"),
|
97 |
gr.inputs.Number(default=512, label="slice_width"),
|
|
|
111 |
]
|
112 |
|
113 |
outputs = [
|
114 |
+
gr.outputs.Image(type="pil", label="Output")
|
|
|
115 |
]
|
116 |
|
117 |
title = "Small Object Detection with SAHI + YOLOv5"
|