Spaces:
Sleeping
Sleeping
File size: 4,564 Bytes
6f5ac87 b345bd5 6f5ac87 bac63be 6f5ac87 bac63be 6f5ac87 bdbb930 6f5ac87 85b23b5 b345bd5 bdbb930 b345bd5 6f5ac87 754c31a 6f5ac87 754c31a 6f5ac87 754c31a 6f5ac87 754c31a 151bcd3 6f5ac87 754c31a 151bcd3 6f5ac87 151bcd3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 |
import gradio as gr
import torch
from sahi.prediction import ObjectPrediction
from sahi.utils.cv import visualize_object_predictions, read_image
from ultralyticsplus import YOLO
image_path = [['test_images/2a998cfb0901db5f8210.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/2ce19ce0191acb44920b.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/2daab6ea3310e14eb801.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/4a137deefb14294a7005 (1).jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/7e77c596436c9132c87d.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/170f914014bac6e49fab.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/3355ec3269c8bb96e2d9.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/546306a88052520c0b43.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/33148464019ed3c08a8f.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/a17a992a1cd0ce8e97c1.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/b5db5e42d8b80ae653a9 (1).jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/b8ee1f5299a84bf612b9.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/b272fec7783daa63f32c.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],['test_images/bb202b3eaec47c9a25d5.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/bf1e22b0a44a76142f5b.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/ea5473c5f53f27617e2e.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45],
['test_images/ee106392e56837366e79.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45], ['test_images/f88d2214a4ee76b02fff.jpg','linhcuem/chamdiem_yolov8_ver10', 640, 0.25, 0.45]]
# Load YOLO model
# model = YOLO('linhcuem/chamdiem_yolov8_ver10')
###################################################
def yolov8_img_inference(
image: gr.inputs.Image = None,
model_path: gr.inputs.Dropdown = None,
image_size: gr.inputs.Slider = 640,
conf_threshold: gr.inputs.Slider = 0.25,
iou_threshold: gr.inputs.Slider = 0.45,
):
model = YOLO(model_path)
model.conf = conf_threshold
model.iou= iou_threshold
# model.overrides['agnostic_nms'] = False # NMS class-agnostic
# model.overrides['max_det'] = 1000
# image = read_image(image)
results = model.predict(image, imgz=image_size, return_outputs=True)
object_prediction_list = []
for _, image_results in enumerate(results):
if len(image_results)!=0:
image_prediction_in_xyxy_format = image_results['det']
for pred in image_prediction_in_xyxy_format:
x1, y1, x2, y2 = (
int(pred[0]),
int(pred[1]),
int(pred[2]),
int(pred[3]),
)
bbox = [x1, y1, x2, y2]
score = pred[4]
category_name = model.model.names[int(pred[5])]
category_id = pred[5]
object_prediction = ObjectPrediction(
bbox=bbox,
category_id=int(category_id),
score=score,
category_name=category_name,
)
object_prediction_list.append(object_prediction)
image = read_image(image)
output_image = visualize_object_predictions(image=image, object_prediction_list=object_prediction_list)
return output_image['image']
inputs_image = [
gr.inputs.Image(type="filepath", label="Input Image"),
gr.inputs.Dropdown(["linhcuem/chamdiem_yolov8_ver10"],
default="linhcuem/chamdiem_yolov8_ver10", label="Model"),
gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold"),
]
outputs_image =gr.outputs.Image(type="filepath", label="Output Image")
title = "Tất cả do anh Đạt"
interface_image = gr.Interface(
fn=yolov8_img_inference,
inputs=inputs_image,
outputs=outputs_image,
title=title,
examples=image_path,
cache_examples=False,
theme='huggingface'
)
demo_app.launch(debug=True, enable_queue=True) |