Spaces:
Sleeping
Sleeping
Create app.py
Browse files
app.py
ADDED
|
@@ -0,0 +1,66 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from typing import Tuple
|
| 2 |
+
from ultralytics import YOLO
|
| 3 |
+
from ultralytics.engine.results import Boxes
|
| 4 |
+
from ultralytics.utils.plotting import Annotator
|
| 5 |
+
|
| 6 |
+
import gradio as gr
|
| 7 |
+
|
| 8 |
+
cell_detector = YOLO("./weights/yolo_uninfected_cells.pt")
|
| 9 |
+
yolo_detector = YOLO("./weights/yolo_infected_cells.pt")
|
| 10 |
+
redetr_detector = YOLO("./weights/redetr_infected_cells.pt")
|
| 11 |
+
|
| 12 |
+
models = {"Yolo V11": yolo_detector, "Real Time Detection Transformer": redetr_detector}
|
| 13 |
+
# classes = {"Yolo V11": [0], "Real Time Detection Transformer": [1]}
|
| 14 |
+
|
| 15 |
+
|
| 16 |
+
def inference(image, model, conf) -> Tuple[str, str, str]:
|
| 17 |
+
bboxes = []
|
| 18 |
+
labels = []
|
| 19 |
+
healthy_cell_count = 0
|
| 20 |
+
unhealthy_cell_count = 0
|
| 21 |
+
cells_results = cell_detector.predict(image, conf=0.4)
|
| 22 |
+
selected_model_results = models[model].predict(
|
| 23 |
+
image, conf=conf
|
| 24 |
+
)
|
| 25 |
+
|
| 26 |
+
for cell_result in cells_results:
|
| 27 |
+
boxes: Boxes = cell_result.boxes
|
| 28 |
+
healthy_cells_bboxes = boxes.xyxy.tolist()
|
| 29 |
+
healthy_cell_count += len(healthy_cells_bboxes)
|
| 30 |
+
bboxes.extend(healthy_cells_bboxes)
|
| 31 |
+
labels.extend(["healthy"] * healthy_cell_count)
|
| 32 |
+
|
| 33 |
+
for res in selected_model_results:
|
| 34 |
+
boxes: Boxes = res.boxes
|
| 35 |
+
unhealthy_cells_bboxes = boxes.xyxy.tolist()
|
| 36 |
+
unhealthy_cell_count += len(unhealthy_cells_bboxes)
|
| 37 |
+
bboxes.extend(unhealthy_cells_bboxes)
|
| 38 |
+
labels.extend(["unhealthy"] * unhealthy_cell_count)
|
| 39 |
+
|
| 40 |
+
annotator = Annotator(image, font_size=5, line_width=1)
|
| 41 |
+
|
| 42 |
+
for box, label in zip(bboxes, labels):
|
| 43 |
+
annotator.box_label(box, label)
|
| 44 |
+
|
| 45 |
+
img = annotator.result()
|
| 46 |
+
return (img, healthy_cell_count, unhealthy_cell_count)
|
| 47 |
+
|
| 48 |
+
|
| 49 |
+
ifer = gr.Interface(
|
| 50 |
+
fn=inference,
|
| 51 |
+
inputs=[
|
| 52 |
+
gr.Image(label="Input Image", type="numpy"),
|
| 53 |
+
gr.Dropdown(
|
| 54 |
+
choices=["Yolo V11", "Real Time Detection Transformer"], multiselect=False, value="Yolo V11"
|
| 55 |
+
),
|
| 56 |
+
gr.Slider(minimum=0.01, maximum=1)
|
| 57 |
+
],
|
| 58 |
+
outputs=[
|
| 59 |
+
gr.Image(label="Output Image", type="numpy"),
|
| 60 |
+
gr.Textbox(label="Healthy Cells Count"),
|
| 61 |
+
gr.Textbox(label="Infected Cells Count"),
|
| 62 |
+
],
|
| 63 |
+
title="Blood Cancer Cell Detection and Counting"
|
| 64 |
+
)
|
| 65 |
+
|
| 66 |
+
ifer.launch(share=True)
|