{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from ultralytics import YOLO\n",
"\n",
"#To initialize a model from scratch:\n",
"#model = YOLO('yolov8n-segreg.yaml')\n",
"\n",
"# To load a pretrained model: (Available pretrained are nano, medium and xlarge)\n",
"model = YOLO('./trained_models/nano.pt')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true,
"tags": []
},
"outputs": [],
"source": [
"# To train the model on a dataset: (GPU recommended)\n",
"\n",
"model.train(data = './datasets/mmc_random/data.yaml', \n",
" mosaic = 1.0,\n",
" hsv_h= 0.5, # (float) image HSV-Hue augmentation (fraction)\n",
" hsv_s= 0.5, # (float) image HSV-Saturation augmentation (fraction)\n",
" hsv_v= 0.5, # (float) image HSV-Value augmentation (fraction)\n",
" degrees= 25.0, # (float) image rotation (+/- deg)\n",
" translate= 0.2, # (float) image translation (+/- fraction)\n",
" scale= 0.75, # (float) image scale (+/- gain)\n",
" shear= 10.0, # (float) image shear (+/- deg)\n",
" perspective= 0.001, # (float) image perspective (+/- fraction), range 0-0.001\n",
" flipud= 0.5, # (float) image flip up-down (probability)\n",
" fliplr= 0.5,\n",
" epochs=1, \n",
" batch=6,\n",
" reg_gain = 1.0, # Weight to deactivate the design variable regression loss in the total loss function.\n",
" amp = True,\n",
" warmup_epochs=0,\n",
" imgsz=int(640),\n",
" workers=12,\n",
" lr0=3e-4,\n",
" cache = \"ram\",\n",
" cos_lr = True,\n",
" single_cls=True,\n",
" rect=False,\n",
" overlap_mask=False,\n",
" mask_ratio=1,\n",
" optimizer = \"AdamW\",\n",
" pretrained=False,\n",
" patience=100,\n",
" weight_decay=1e-2, \n",
" val=True,\n",
" resume=False,\n",
" device='cpu',\n",
" plots=True\n",
" )"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"\n"
]
},
{
"ename": "KeyboardInterrupt",
"evalue": "",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[5], line 10\u001b[0m\n\u001b[1;32m 8\u001b[0m image \u001b[38;5;241m=\u001b[39m preprocess_image(image_path, threshold_value\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.9\u001b[39m, upscale\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m)\n\u001b[1;32m 9\u001b[0m \u001b[38;5;66;03m# Run the model\u001b[39;00m\n\u001b[0;32m---> 10\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mrun_model\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miou\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0.3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimgsz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m320\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 11\u001b[0m \u001b[38;5;66;03m# Process the results\u001b[39;00m\n\u001b[1;32m 13\u001b[0m input_image_array_tensor, seg_result, pred_Phi, sum_pred_H, final_H, dice_loss, tversky_loss \u001b[38;5;241m=\u001b[39m process_results(results, image)\n",
"File \u001b[0;32m~/Documents/GitHub/YOLOv8-TO/./utils/yolo_utils.py:146\u001b[0m, in \u001b[0;36mrun_model\u001b[0;34m(model, image, conf, iou, imgsz)\u001b[0m\n\u001b[1;32m 145\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mrun_model\u001b[39m(model, image, conf\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.05\u001b[39m, iou\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.5\u001b[39m, imgsz\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m640\u001b[39m):\n\u001b[0;32m--> 146\u001b[0m results \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43mimage\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconf\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mconf\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43miou\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43miou\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mimgsz\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mimgsz\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 147\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m results\n",
"File \u001b[0;32m~/Documents/GitHub/YOLOv8-TO/yolov8-to/ultralytics/engine/model.py:96\u001b[0m, in \u001b[0;36mModel.__call__\u001b[0;34m(self, source, stream, **kwargs)\u001b[0m\n\u001b[1;32m 94\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, source\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, stream\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 95\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"Calls the 'predict' function with given arguments to perform object detection.\"\"\"\u001b[39;00m\n\u001b[0;32m---> 96\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredict\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/opt/anaconda3/envs/yolov8to/lib/python3.11/site-packages/torch/utils/_contextlib.py:115\u001b[0m, in \u001b[0;36mcontext_decorator.<locals>.decorate_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 112\u001b[0m \u001b[38;5;129m@functools\u001b[39m\u001b[38;5;241m.\u001b[39mwraps(func)\n\u001b[1;32m 113\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mdecorate_context\u001b[39m(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m 114\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m--> 115\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/GitHub/YOLOv8-TO/yolov8-to/ultralytics/engine/model.py:238\u001b[0m, in \u001b[0;36mModel.predict\u001b[0;34m(self, source, stream, predictor, **kwargs)\u001b[0m\n\u001b[1;32m 236\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m prompts \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mset_prompts\u001b[39m\u001b[38;5;124m'\u001b[39m): \u001b[38;5;66;03m# for SAM-type models\u001b[39;00m\n\u001b[1;32m 237\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor\u001b[38;5;241m.\u001b[39mset_prompts(prompts)\n\u001b[0;32m--> 238\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpredictor\u001b[38;5;241m.\u001b[39mpredict_cli(source\u001b[38;5;241m=\u001b[39msource) \u001b[38;5;28;01mif\u001b[39;00m is_cli \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpredictor\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mstream\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mstream\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/Documents/GitHub/YOLOv8-TO/yolov8-to/ultralytics/engine/predictor.py:194\u001b[0m, in \u001b[0;36mBasePredictor.__call__\u001b[0;34m(self, source, model, stream, *args, **kwargs)\u001b[0m\n\u001b[1;32m 192\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstream_inference(source, model, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m 193\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 194\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mlist\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mstream_inference\u001b[49m\u001b[43m(\u001b[49m\u001b[43msource\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/opt/anaconda3/envs/yolov8to/lib/python3.11/site-packages/torch/utils/_contextlib.py:35\u001b[0m, in \u001b[0;36m_wrap_generator.<locals>.generator_context\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 32\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# Issuing `None` to a generator fires it up\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m ctx_factory():\n\u001b[0;32m---> 35\u001b[0m response \u001b[38;5;241m=\u001b[39m \u001b[43mgen\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msend\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m 37\u001b[0m \u001b[38;5;28;01mwhile\u001b[39;00m \u001b[38;5;28;01mTrue\u001b[39;00m:\n\u001b[1;32m 38\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 39\u001b[0m \u001b[38;5;66;03m# Forward the response to our caller and get its next request\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/GitHub/YOLOv8-TO/yolov8-to/ultralytics/engine/predictor.py:257\u001b[0m, in \u001b[0;36mBasePredictor.stream_inference\u001b[0;34m(self, source, model, *args, **kwargs)\u001b[0m\n\u001b[1;32m 255\u001b[0m \u001b[38;5;66;03m# Postprocess\u001b[39;00m\n\u001b[1;32m 256\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m profilers[\u001b[38;5;241m2\u001b[39m]:\n\u001b[0;32m--> 257\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mresults \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpostprocess\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mim\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mim0s\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 258\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_callbacks(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mon_predict_postprocess_end\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 260\u001b[0m \u001b[38;5;66;03m# Visualize, save, write results\u001b[39;00m\n",
"File \u001b[0;32m~/Documents/GitHub/YOLOv8-TO/yolov8-to/ultralytics/models/yolo/segment/predict.py:48\u001b[0m, in \u001b[0;36mSegmentationPredictor.postprocess\u001b[0;34m(self, preds, img, orig_imgs)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mpostprocess\u001b[39m(\u001b[38;5;28mself\u001b[39m, preds, img, orig_imgs):\n\u001b[1;32m 46\u001b[0m \u001b[38;5;66;03m#print(preds[0].shape)\u001b[39;00m\n\u001b[1;32m 47\u001b[0m regression_preds \u001b[38;5;241m=\u001b[39m preds[\u001b[38;5;241m1\u001b[39m][\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\n\u001b[0;32m---> 48\u001b[0m p, final_reg \u001b[38;5;241m=\u001b[39m \u001b[43mops\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnon_max_suppression\u001b[49m\u001b[43m(\u001b[49m\u001b[43mprediction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mpreds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 49\u001b[0m \u001b[43m \u001b[49m\u001b[43mmask_coef\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 50\u001b[0m \u001b[43m \u001b[49m\u001b[43mproto\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mpreds\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 51\u001b[0m \u001b[43m \u001b[49m\u001b[43mimg_shape\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mimg\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mshape\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 52\u001b[0m \u001b[43m \u001b[49m\u001b[43mconf_thres\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconf\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 53\u001b[0m \u001b[43m \u001b[49m\u001b[43miou_thres\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miou\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 54\u001b[0m \u001b[43m \u001b[49m\u001b[43magnostic\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43magnostic_nms\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 55\u001b[0m \u001b[43m \u001b[49m\u001b[43mmax_det\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmax_det\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 56\u001b[0m \u001b[43m \u001b[49m\u001b[43mnc\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mlen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mnames\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 57\u001b[0m \u001b[43m \u001b[49m\u001b[43mregression_var\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mregression_preds\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 58\u001b[0m \u001b[43m \u001b[49m\u001b[43mclasses\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mclasses\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 59\u001b[0m \u001b[38;5;66;03m#print(p[0].shape)\u001b[39;00m\n\u001b[1;32m 60\u001b[0m results \u001b[38;5;241m=\u001b[39m []\n",
"File \u001b[0;32m~/Documents/GitHub/YOLOv8-TO/yolov8-to/ultralytics/utils/ops.py:329\u001b[0m, in \u001b[0;36mnon_max_suppression\u001b[0;34m(prediction, mask_coef, proto, img_shape, regression_var, conf_thres, iou_thres, classes, agnostic, multi_label, labels, max_det, nc, max_time_img, max_nms, max_wh)\u001b[0m\n\u001b[1;32m 327\u001b[0m \u001b[38;5;66;03m# Compute intersection and union for the current batch\u001b[39;00m\n\u001b[1;32m 328\u001b[0m intersection_batch \u001b[38;5;241m=\u001b[39m (masks_bool_batch\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;241m&\u001b[39m masks_bool\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m))\u001b[38;5;241m.\u001b[39mfloat()\u001b[38;5;241m.\u001b[39msum(dim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[0;32m--> 329\u001b[0m union_batch \u001b[38;5;241m=\u001b[39m \u001b[43m(\u001b[49m\u001b[43mmasks_bool_batch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m|\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mmasks_bool\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munsqueeze\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfloat\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39msum(dim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m))\n\u001b[1;32m 331\u001b[0m \u001b[38;5;66;03m# Calculate areas for IoMin\u001b[39;00m\n\u001b[1;32m 332\u001b[0m area_batch \u001b[38;5;241m=\u001b[39m masks_bool_batch\u001b[38;5;241m.\u001b[39mfloat()\u001b[38;5;241m.\u001b[39msum(dim\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m2\u001b[39m))\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m1\u001b[39m) \u001b[38;5;66;03m# Areas of masks in the current batch\u001b[39;00m\n",
"\u001b[0;31mKeyboardInterrupt\u001b[0m: "
]
}
],
"source": [
"import sys\n",
"sys.path.append('./utils')\n",
"\n",
"from yolo_utils import preprocess_image, run_model, process_results, plot_results\n",
"\n",
"# Load and preprocess the image\n",
"image_path = './test.png'\n",
"image = preprocess_image(image_path, threshold_value=0.9, upscale=False)\n",
"# Run the model\n",
"results = run_model(model, image, conf=0.1, iou=0.3, imgsz=640)\n",
"# Process the results\n",
"\n",
"input_image_array_tensor, seg_result, pred_Phi, sum_pred_H, final_H, dice_loss, tversky_loss = process_results(results, image)\n",
"# Plot the results\n",
"plot_results(input_image_array_tensor, seg_result, pred_Phi, sum_pred_H, final_H, dice_loss, tversky_loss, filename='output_plot.png')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "customyolo",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.9"
}
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"nbformat": 4,
"nbformat_minor": 4
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