import os import cv2 import io import pandas as pd import PIL.Image as Image import gradio as gr import numpy as np import matplotlib.pyplot as plt import math import time from sklearn.neighbors import KernelDensity from pathlib import Path from ultralytics import ASSETS, YOLO from sklearn.model_selection import GridSearchCV 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, img_h, img_w, 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_col = [] afm_image = [] cno_image = [] kde_image = [] file_name = [] ecti_score = [] # total_layer_area = [] # total_layer_cno = [] # total_layer_density = [] # avg_area_col = [] # total_area_col = [] for idx, result in enumerate(results): cno = len(result.boxes) file_label = img[idx].split(os.sep)[-1] # single_layer_area = [] # single_layer_cno = [] single_layer_density = [] total_area = 0 if cno < 5: # avg_area_col.append(np.nan) # total_area_col.append(np.nan) # nan_arr = np.empty([25]) # nan_arr[:] = np.nan ecti_score.append(np.nan) # total_layer_area.append(nan_arr) # total_layer_cno.append(nan_arr) # total_layer_density.append(nan_arr) else: 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(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 afm_image.append([img[idx], file_label]) cno_image.append([Image.fromarray(im_array[..., ::-1]), file_label]) cno_count.append(cno) file_name.append(file_label) ### ============================ kde = KernelDensity(metric='euclidean', kernel='gaussian', algorithm='ball_tree') # Finding Optimal Bandwidth ti = time.time() if cno < 7: fold = cno else: fold = 7 gs = GridSearchCV(kde, {'bandwidth': np.linspace(20, 60, 41)}, cv=fold) cv = gs.fit(cno_coor) bw = cv.best_params_['bandwidth'] tf = time.time() print("Finding optimal bandwidth={:.2f} ({:n}-fold cross-validation): {:.2f} secs".format(bw, cv.cv, (tf - ti))) kde.bandwidth = bw _ = kde.fit(cno_coor) xgrid = np.arange(0, result.orig_img.shape[1], 1) ygrid = np.arange(0, result.orig_img.shape[0], 1) xv, yv = np.meshgrid(xgrid, ygrid) xys = np.vstack([xv.ravel(), yv.ravel()]).T gdim = xv.shape zi = np.arange(xys.shape[0]) zXY = xys z = np.exp(kde.score_samples(zXY)) zg = -9999 + np.zeros(xys.shape[0]) zg[zi] = z xyz = np.hstack((xys[:, :2], zg[:, None])) x = xyz[:, 0].reshape(gdim) y = xyz[:, 1].reshape(gdim) z = xyz[:, 2].reshape(gdim) levels = np.linspace(0, z.max(), 26) print("levels", levels) for j in range(len(levels) - 1): area = np.argwhere(z >= levels[j]) area_concatenate = numcat(area) CNO_concatenate = numcat(cno_coor) ecno = np.count_nonzero(np.isin(area_concatenate, CNO_concatenate)) layer_area = area.shape[0] if layer_area == 0: density = np.round(0.0, 4) else: density = np.round((ecno / layer_area) * result.orig_img.shape[0] * result.orig_img.shape[1] / (img_h * img_w), 4) print("Level {}: Area={}, CNO={}, density={}".format(j, layer_area, ecno, density)) # single_layer_area.append(layer_area) # single_layer_cno.append(ecno) single_layer_density.append(density) # total_layer_area.append(single_layer_area) # total_layer_cno.append(single_layer_cno) # total_layer_density.append(single_layer_density) # print(sum_range(single_layer_density, 10, 14)) # print("deb ", single_layer_density) ecti_score.append(np.round(sum_range(single_layer_density, 10, 14) / 5.0, 2)) # Plot CNO Distribution plt.contourf(x, y, z, levels=levels, cmap=plt.cm.bone) plt.axis('off') # plt.gcf().set_size_inches(8, 8) plt.gcf().set_size_inches(8 * (gdim[1] / gdim[0]), 8) plt.gca().invert_yaxis() plt.xlim(0, gdim[1] - 1) plt.ylim(gdim[0] - 1, 0) plt.plot() img_buf = io.BytesIO() plt.savefig(img_buf, format='png', bbox_inches='tight', pad_inches=0) kde_im = Image.open(img_buf) kde_image.append([kde_im, file_label]) data = { "Files": file_name, "CNO Count": cno_count, "ECTI Score": ecti_score } # load data into a DataFrame object: cno_df = pd.DataFrame(data) return cno_df, afm_image, cno_image, kde_image def numcat(arr): arr_size = arr.shape[0] arr_cat = np.empty([arr_size, 1], dtype=np.int32) for i in range(arr.shape[0]): arr_cat[i] = arr[i][0] * 1000 + arr[i][1] return arr_cat 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 = "" img_h = 20 img_w = 20 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 = [] afm_gallery = [] cno_gallery = [] test_label = "" return name_textbox, img_h, img_w, gender_radio, age_slider, fitzpatrick, history, model_radio, input_files, conf_slider, \ iou_slider, analysis_results, afm_gallery, cno_gallery, test_label def sum_range(l,a,b): s = 0 for i in range(a,b+1): s += l[i] return s 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): with gr.Row(): name_textbox = gr.Textbox(label="Sample") with gr.Row(): img_h = gr.Number(label="Image Height (μm)", value=20, interactive=True) img_w = gr.Number(label="Image Width (μm)", value=20, interactive=True) 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") with gr.Tab("AFM"): afm_gallery = gr.Gallery(label="Result", show_label=True, columns=3, object_fit="contain") with gr.Tab("CNO"): cno_gallery = gr.Gallery(label="Result", show_label=True, columns=3, object_fit="contain") with gr.Tab("KDE"): kde_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, img_h, img_w, model_radio, input_files, conf_slider, iou_slider], outputs=[analysis_results, afm_gallery, cno_gallery, kde_gallery] ) clear_btn.click(reset, outputs=[name_textbox, img_h, img_w, gender_radio, age_slider, fitzpatrick, history, model_radio, input_files, conf_slider, iou_slider, analysis_results, afm_gallery, cno_gallery, test_label]) afm_gallery.select(highlight_df, inputs=analysis_results, outputs=[test_label, analysis_results]) 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(share=False, auth=[('jenhw', 'admin'), ('user', 'admin')], # auth_message="Enter your username and password") app.launch(share=False)