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)