File size: 2,140 Bytes
2cd5935 a0ba42d 2cd5935 05d0642 2cd5935 05d0642 54432a3 2cd5935 54432a3 2cd5935 54432a3 2cd5935 54432a3 2cd5935 54432a3 2cd5935 54432a3 |
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 |
from typing import Tuple
from ultralytics import YOLO
from ultralytics.engine.results import Boxes
from ultralytics.utils.plotting import Annotator
import gradio as gr
cell_detector = YOLO("./weights/yolo_uninfected_cells.pt")
yolo_detector = YOLO("./weights/yolo_infected_cells.pt")
redetr_detector = YOLO("./weights/redetr_infected_cells.pt")
models = {"Yolo V11": yolo_detector, "Real Time Detection Transformer": redetr_detector}
# classes = {"Yolo V11": [0], "Real Time Detection Transformer": [1]}
def inference(image, model, conf) -> Tuple[str, str, str]:
bboxes = []
labels = []
healthy_cell_count = 0
unhealthy_cell_count = 0
cells_results = cell_detector.predict(image, conf=0.4)
selected_model_results = models[model].predict(
image, conf=conf
)
for cell_result in cells_results:
boxes: Boxes = cell_result.boxes
healthy_cells_bboxes = boxes.xyxy.tolist()
healthy_cell_count += len(healthy_cells_bboxes)
bboxes.extend(healthy_cells_bboxes)
labels.extend(["healthy"] * healthy_cell_count)
for res in selected_model_results:
boxes: Boxes = res.boxes
unhealthy_cells_bboxes = boxes.xyxy.tolist()
unhealthy_cell_count += len(unhealthy_cells_bboxes)
bboxes.extend(unhealthy_cells_bboxes)
labels.extend(["unhealthy"] * unhealthy_cell_count)
annotator = Annotator(image, font_size=5, line_width=1)
for box, label in zip(bboxes, labels):
annotator.box_label(box, label)
img = annotator.result()
return (img, healthy_cell_count, unhealthy_cell_count)
ifer = gr.Interface(
fn=inference,
inputs=[
gr.Image(label="Input Image", type="numpy"),
gr.Dropdown(
choices=["Yolo V11", "Real Time Detection Transformer"], multiselect=False
),
gr.Slider(minimum=0.01, maximum=1)
],
outputs=[
gr.Image(label="Output Image", type="numpy"),
gr.Textbox(label="Healthy Cells Count"),
gr.Textbox(label="Infected Cells Count"),
],
title="Blood Cancer Cell Detection and Counting"
)
ifer.launch(share=True) |