import os import cv2 import pandas as pd import PIL.Image as Image import gradio as gr import numpy as np import math from pathlib import Path from ultralytics import ASSETS, YOLO DIR_NAME = Path(os.path.dirname(__file__)) DETECTION_MODEL_n = os.path.join(DIR_NAME, 'models', 'YOLOv8-N_CNO_Detection.pt') DETECTION_MODEL_s = os.path.join(DIR_NAME, 'models', 'YOLOv8-S_CNO_Detection.pt') DETECTION_MODEL_m = os.path.join(DIR_NAME, 'models', 'YOLOv8-M_CNO_Detection.pt') DETECTION_MODEL_l = os.path.join(DIR_NAME, 'models', 'YOLOv8-L_CNO_Detection.pt') DETECTION_MODEL_x = os.path.join(DIR_NAME, 'models', 'YOLOv8-X_CNO_Detection.pt') # MODEL = os.path.join(DIR_NAME, 'models', 'YOLOv8-M_CNO_Detection.pt') # model = YOLO(MODEL) # cno_df = pd.DataFrame() def predict_image(name, model, img, conf_threshold, iou_threshold): """Predicts and plots labeled objects in an image using YOLOv8 model with adjustable confidence and IOU thresholds.""" gr.Info("Starting process") # gr.Warning("Name is empty") if name == "": gr.Warning("Name is empty") if model == 'YOLOv8-N': CNO_model = YOLO(DETECTION_MODEL_n) elif model == 'YOLOv8-S': CNO_model = YOLO(DETECTION_MODEL_s) elif model == 'YOLOv8-M': CNO_model = YOLO(DETECTION_MODEL_m) elif model == 'YOLOv8-L': CNO_model = YOLO(DETECTION_MODEL_l) else: CNO_model = YOLO(DETECTION_MODEL_x) results = CNO_model.predict( source=img, conf=conf_threshold, iou=iou_threshold, show_labels=False, show_conf=False, imgsz=512, max_det=1200 ) cno_count = [] cno_image = [] file_name = [] # print("deb", img) for idx, result in enumerate(results): cno = len(result.boxes) cno_coor = np.empty([cno, 2], dtype=int) file_label = img[idx].split(os.sep)[-1] for j in range(cno): # w = r.boxes.xywh[j][2] # h = r.boxes.xywh[j][3] # area = (math.pi * w * h / 4) * 20 * 20 / (512 * 512) # total_area += area # bbox_img = r.orig_img x = round(result.boxes.xywh[j][0].item()) y = round(result.boxes.xywh[j][1].item()) x1 = round(result.boxes.xyxy[j][0].item()) y1 = round(result.boxes.xyxy[j][1].item()) x2 = round(result.boxes.xyxy[j][2].item()) y2 = round(result.boxes.xyxy[j][3].item()) cno_coor[j] = [x, y] cv2.rectangle(result.orig_img, (x1, y1), (x2, y2), (0, 255, 0), 1) im_array = result.orig_img cno_image.append([Image.fromarray(im_array[..., ::-1]), file_label]) cno_count.append(cno) file_name.append(file_label) """ for r in results: CNO = len(r.boxes) CNO_coor = np.empty([CNO, 2], dtype=int) for j in range(CNO): # w = r.boxes.xywh[j][2] # h = r.boxes.xywh[j][3] # area = (math.pi * w * h / 4) * 20 * 20 / (512 * 512) # total_area += area # bbox_img = r.orig_img x = round(r.boxes.xywh[j][0].item()) y = round(r.boxes.xywh[j][1].item()) x1 = round(r.boxes.xyxy[j][0].item()) y1 = round(r.boxes.xyxy[j][1].item()) x2 = round(r.boxes.xyxy[j][2].item()) y2 = round(r.boxes.xyxy[j][3].item()) CNO_coor[j] = [x, y] cv2.rectangle(r.orig_img, (x1, y1), (x2, y2), (0, 255, 0), 1) im_array = r.orig_img im = Image.fromarray(im_array[..., ::-1]) CNO_count = "CNO Count: " + str(CNO) test = [] for i in range(len(cno_image)): test.append([cno_image[0], f"label {i}"]) """ data = { "Files": file_name, "CNO Count": cno_count, } # load data into a DataFrame object: cno_df = pd.DataFrame(data) return cno_df, cno_image def highlight_max(s, props=''): return np.where(s == np.nanmax(s.values), props, '') def highlight_df(df, data: gr.SelectData): styler = df.style.apply(lambda x: ['background: lightgreen' if x.Files == data.value["caption"] else None for i in x], axis=1) # print("selected", data.value["caption"]) return data.value["caption"], styler def reset(): name_textbox = "" gender_radio = None age_slider = 0 fitzpatrick = 1 history = [] model_radio = "YOLOv8-M" input_files = [] conf_slider = 0.2 iou_slider = 0.5 analysis_results = [] cno_gallery = [] test_label = "" return name_textbox, gender_radio, age_slider, fitzpatrick, history, model_radio, input_files, conf_slider, iou_slider, analysis_results, cno_gallery, test_label with gr.Blocks(title="AFM AI Analysis", theme="default") as app: with gr.Row(): with gr.Column(): # gr.Markdown("User Information") with gr.Accordion("User Information", open=True): name_textbox = gr.Textbox(label="Name") with gr.Row(): gender_radio = gr.Radio(["Male", "Female"], label="Gender", interactive=True, scale=1) age_slider = gr.Slider(minimum=0, maximum=100, step=1, value=0, label="Age", interactive=True, scale=2) with gr.Group(): fitzpatrick = gr.Slider(minimum=1, maximum=6, step=1, value=1, label="Fitzpatrick", interactive=True) history = gr.Checkboxgroup(["Familial Disease", "Allergic Rhinitis", "Asthma"], label="Medical History", interactive=True) input_files = gr.File(file_types=["image"], file_count="multiple", label="Upload Image") # gr.Markdown("Model Configuration") with gr.Accordion("Model Configuration", open=False): model_radio = gr.Radio(["YOLOv8-N", "YOLOv8-S", "YOLOv8-M", "YOLOv8-L", "YOLOv8-X"], label="Model Selection", value="YOLOv8-M") conf_slider = gr.Slider(minimum=0, maximum=1, value=0.2, label="Confidence threshold") iou_slider = gr.Slider(minimum=0, maximum=1, value=0.5, label="IoU threshold") with gr.Row(): analyze_btn = gr.Button("Analyze") clear_btn = gr.Button("Reset") with gr.Column(): analysis_results = gr.Dataframe(headers=["Files", "CNO Count"], interactive=False) # cno_label = gr.Label(label="Analysis Results") cno_gallery = gr.Gallery(label="Result", show_label=True, columns=3, object_fit="contain") test_label = gr.Label(label="Analysis Results") # cno_img = gr.Image(type="pil", label="Result") analyze_btn.click( fn=predict_image, inputs=[name_textbox, model_radio, input_files, conf_slider, iou_slider], outputs=[analysis_results, cno_gallery] ) clear_btn.click(reset, outputs=[name_textbox, gender_radio, age_slider, fitzpatrick, history, model_radio, input_files, conf_slider, iou_slider, analysis_results, cno_gallery, test_label]) cno_gallery.select(highlight_df, inputs=analysis_results, outputs=[test_label, analysis_results]) """ iface = gr.Interface( fn=predict_image, inputs=[ gr.Textbox(label="User Name"), gr.Radio(["YOLOv8-N", "YOLOv8-S", "YOLOv8-M", "YOLOv8-L", "YOLOv8-X"], value="YOLOv8-M"), # gr.Image(type="filepath", label="Upload Image"), gr.File(file_types=["image"], file_count="multiple", label="Upload Image"), gr.Slider(minimum=0, maximum=1, value=0.2, label="Confidence threshold"), gr.Slider(minimum=0, maximum=1, value=0.5, label="IoU threshold") ], outputs=[gr.Label(label="Analysis Results"), gr.Image(type="pil", label="Result")], title="AFM AI Analysis", description="Upload images for inference. The YOLOv8-M model is used by default.", theme=gr.themes.Default() ) """ if __name__ == '__main__': # iface.launch() app.launch(auth=('user', 'admin'), auth_message="Enter your username and password")