diff --git a/.gitignore b/.gitignore index b46291c..233e08e 100644 --- a/.gitignore +++ b/.gitignore @@ -1 +1,3 @@ virtualenv/ +dataset-obj365/ +dataset-rubick/ diff --git a/download.py b/download.py new file mode 100644 index 0000000..cd8ab91 --- /dev/null +++ b/download.py @@ -0,0 +1,58 @@ +from tqdm import tqdm + +from ultralytics.yolo.utils.checks import check_requirements +from ultralytics.yolo.utils.downloads import download +from ultralytics.yolo.utils.ops import xyxy2xywhn + +import numpy as np +from pathlib import Path + +check_requirements(('pycocotools>=2.0',)) +from pycocotools.coco import COCO + +# Make Directories +dir = Path(yaml['path']) # dataset root dir +for p in 'images', 'labels': + (dir / p).mkdir(parents=True, exist_ok=True) + for q in 'train', 'val': + (dir / p / q).mkdir(parents=True, exist_ok=True) + +# Train, Val Splits +for split, patches in [('train', 50 + 1), ('val', 43 + 1)]: + print(f"Processing {split} in {patches} patches ...") + images, labels = dir / 'images' / split, dir / 'labels' / split + + # Download + url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/" + if split == 'train': + download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir) # annotations json + download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, threads=8) + elif split == 'val': + download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir) # annotations json + download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, threads=8) + download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, threads=8) + + # Move + for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'): + f.rename(images / f.name) # move to /images/{split} + + # Labels + coco = COCO(dir / f'zhiyuan_objv2_{split}.json') + names = [x["name"] for x in coco.loadCats(coco.getCatIds())] + for cid, cat in enumerate(names): + catIds = coco.getCatIds(catNms=[cat]) + imgIds = coco.getImgIds(catIds=catIds) + for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'): + width, height = im["width"], im["height"] + path = Path(im["file_name"]) # image filename + try: + with open(labels / path.with_suffix('.txt').name, 'a') as file: + annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=None) + for a in coco.loadAnns(annIds): + x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner) + xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4) + x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped + file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n") + except Exception as e: + print(e) + diff --git a/example/demo.py b/example/demo.py index a954e20..bf014fc 100644 --- a/example/demo.py +++ b/example/demo.py @@ -1,53 +1,20 @@ from ultralytics import YOLO # from ultralytics.yolo.utils.benchmarks import benchmark -import cv2 # Load a model # model = YOLO("yolov8n.yaml") # build a new model from scratch -model = YOLO("yolov8n.pt") # load a pretrained model (recommended for training) +# model = YOLO("../runs/detect/train3/weights/best.pt") # zhou +model = YOLO("../runs/detect/train4/weights/best.pt") # 1000 img, mine +model = YOLO("../runs/detect/train2/weights/best.pt") # 1000 img, based on 3000 img +# model = YOLO("../../../project/runs/detect/train3/weights/best.pt") # 3000 img, mine + # Use the model # model.train(data="coco128.yaml", epochs=3,workers=0) # train the model,workers=0 if windows # metrics = model.val() # evaluate model performance on the validation set -''' -results = model("bus.jpg") # predict on an image -print(results) -# ''' -img_path = "./image/" -# results = model.predict(img_path, save=True,conf=0.5) # device=0 by default, conf:置信度阈值 +img_path = "./image" +results = model.predict(img_path, save = True) # device=0 by default, conf:置信度阈值 # results = model.predict(img_path,save=True,classes=[0,2],conf=0.5) # i.e. classes=0,classes=[0,3,4] # save detection results * # results = model.predict(img_path,save=True,save_txt=True,classes=0,conf=0.4) - - - -# predict video -video_path = "./video/1.mp4" -cap = cv2.VideoCapture(0) - -# Loop through the video frames -while cap.isOpened(): - # Read a frame from the video - success, frame = cap.read() - - if success: - # Run YOLOv8 inference on the frame - results = model(frame) - - # Visualize the results on the frame - annotated_frame = results[0].plot() - - # Display the annotated frame - cv2.imshow("YOLOv8 Inference", annotated_frame) - - # Break the loop if 'q' is pressed - if cv2.waitKey(1) & 0xFF == ord("q"): - break - else: - # Break the loop if the end of the video is reached - break - -# Release the video capture object and close the display window -cap.release() -cv2.destroyAllWindows() diff --git a/example/image/182786930_7ea28fa4e5_b.jpg b/example/image/182786930_7ea28fa4e5_b.jpg new file mode 100644 index 0000000..04ec654 Binary files 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files /dev/null and b/example/image/sasd.jpg differ diff --git a/example/video.py b/example/video.py new file mode 100644 index 0000000..723c1c7 --- /dev/null +++ b/example/video.py @@ -0,0 +1,39 @@ +import cv2 +from ultralytics import YOLO + +# Load the YOLOv8 model +# model = YOLO("../runs/detect/train3/weights/best.pt") # zhou +# model = YOLO("../runs/detect/train4/weights/best.pt") # 1000 img, mine +# model = YOLO("../runs/detect/train2/weights/best.pt") # 1000 img, based on 3000 img +model = YOLO("../../../project/runs/detect/train3/weights/best.pt") # 3000 img, mine + + +# Open the video file +video_path = "./video/demo_video_no_detection.mp4" +cap = cv2.VideoCapture(video_path) + +# Loop through the video frames +while cap.isOpened(): + # Read a frame from the video + success, frame = cap.read() + + if success: + # Run YOLOv8 inference on the frame + results = model(frame) + + # Visualize the results on the frame + annotated_frame = results[0].plot() + + # Display the annotated frame + cv2.imshow("YOLOv8 Inference", annotated_frame) + + # Break the loop if 'q' is pressed + if cv2.waitKey(1) & 0xFF == ord("q"): + break + else: + # Break the loop if the end of the video is reached + break + +# Release the video capture object and close the display window +cap.release() +cv2.destroyAllWindows() diff --git a/example/video/demo_keys.mp4 b/example/video/demo_keys.mp4 new file mode 100644 index 0000000..a205603 Binary files /dev/null and b/example/video/demo_keys.mp4 differ diff --git a/example/video/demo_video_no_detection.mp4 b/example/video/demo_video_no_detection.mp4 new file mode 100644 index 0000000..a48a569 Binary files /dev/null and b/example/video/demo_video_no_detection.mp4 differ diff --git a/example/yolov8n.pt b/example/yolov8n.pt new file mode 100644 index 0000000..d61ef50 Binary files /dev/null and b/example/yolov8n.pt differ diff --git a/runs/detect/train2/F1_curve.png b/runs/detect/train2/F1_curve.png new file mode 100644 index 0000000..9e74ea3 Binary files /dev/null and b/runs/detect/train2/F1_curve.png differ diff --git a/runs/detect/train2/PR_curve.png b/runs/detect/train2/PR_curve.png new file mode 100644 index 0000000..6028836 Binary files /dev/null and b/runs/detect/train2/PR_curve.png differ diff --git a/runs/detect/train2/P_curve.png b/runs/detect/train2/P_curve.png new file mode 100644 index 0000000..89278f7 Binary files /dev/null and b/runs/detect/train2/P_curve.png differ diff --git a/runs/detect/train2/R_curve.png b/runs/detect/train2/R_curve.png new file mode 100644 index 0000000..cbee2a1 Binary files /dev/null and b/runs/detect/train2/R_curve.png differ diff --git a/runs/detect/train2/args.yaml b/runs/detect/train2/args.yaml new file mode 100644 index 0000000..92eef19 --- /dev/null +++ b/runs/detect/train2/args.yaml @@ -0,0 +1,97 @@ +task: detect +mode: train +model: ../../project/runs/detect/train3/weights/best.pt +data: ../datasets/zcxv.v2i.yolov8/data.yaml +epochs: 100 +patience: 50 +batch: 16 +imgsz: 640 +save: true +save_period: -1 +cache: false +device: cuda +workers: 8 +project: null +name: null +exist_ok: false +pretrained: true +optimizer: auto +verbose: true +seed: 0 +deterministic: true +single_cls: false +rect: false +cos_lr: false +close_mosaic: 0 +resume: false +amp: true +fraction: 1.0 +profile: false +overlap_mask: true +mask_ratio: 4 +dropout: 0.0 +val: true +split: val +save_json: false +save_hybrid: false +conf: null +iou: 0.7 +max_det: 300 +half: false +dnn: false +plots: true +source: null +show: false +save_txt: false +save_conf: false +save_crop: false +show_labels: true +show_conf: true +vid_stride: 1 +line_width: null +visualize: false +augment: false +agnostic_nms: false +classes: null +retina_masks: false +boxes: true +format: torchscript +keras: false +optimize: false +int8: false +dynamic: false +simplify: false +opset: null +workspace: 4 +nms: false +lr0: 0.01 +lrf: 0.01 +momentum: 0.937 +weight_decay: 0.0005 +warmup_epochs: 3.0 +warmup_momentum: 0.8 +warmup_bias_lr: 0.1 +box: 7.5 +cls: 0.5 +dfl: 1.5 +pose: 12.0 +kobj: 1.0 +label_smoothing: 0.0 +nbs: 64 +hsv_h: 0.015 +hsv_s: 0.7 +hsv_v: 0.4 +degrees: 0.0 +translate: 0.1 +scale: 0.5 +shear: 0.0 +perspective: 0.0 +flipud: 0.0 +fliplr: 0.5 +mosaic: 1.0 +mixup: 0.0 +copy_paste: 0.0 +cfg: null +v5loader: false +tracker: botsort.yaml +save_dir: /home/ryan/Documents/School/2023t1/dnb/project/runs/detect/train2 diff --git a/runs/detect/train2/confusion_matrix.png b/runs/detect/train2/confusion_matrix.png new file mode 100644 index 0000000..cbe2970 Binary files /dev/null and b/runs/detect/train2/confusion_matrix.png differ diff --git a/runs/detect/train2/confusion_matrix_normalized.png b/runs/detect/train2/confusion_matrix_normalized.png new file mode 100644 index 0000000..001de62 Binary files /dev/null and b/runs/detect/train2/confusion_matrix_normalized.png differ diff --git a/runs/detect/train2/labels.jpg b/runs/detect/train2/labels.jpg new file mode 100644 index 0000000..094e99a Binary 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4.2584e-05, 4.2584e-05, 4.2584e-05 diff --git a/runs/detect/train4/results.png b/runs/detect/train4/results.png new file mode 100644 index 0000000..5527f48 Binary files /dev/null and b/runs/detect/train4/results.png differ diff --git a/runs/detect/train4/train_batch0.jpg b/runs/detect/train4/train_batch0.jpg new file mode 100644 index 0000000..f360b2f Binary files /dev/null and b/runs/detect/train4/train_batch0.jpg differ diff --git a/runs/detect/train4/train_batch1.jpg b/runs/detect/train4/train_batch1.jpg new file mode 100644 index 0000000..a7381c1 Binary files /dev/null and b/runs/detect/train4/train_batch1.jpg differ diff --git a/runs/detect/train4/train_batch2.jpg b/runs/detect/train4/train_batch2.jpg new file mode 100644 index 0000000..8723090 Binary files /dev/null and b/runs/detect/train4/train_batch2.jpg differ diff --git a/runs/detect/train4/val_batch0_labels.jpg b/runs/detect/train4/val_batch0_labels.jpg new file mode 100644 index 0000000..1777378 Binary files /dev/null and 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b/runs/detect/train4/val_batch2_pred.jpg new file mode 100644 index 0000000..154d69b Binary files /dev/null and b/runs/detect/train4/val_batch2_pred.jpg differ diff --git a/runs/detect/train4/weights/best.pt b/runs/detect/train4/weights/best.pt new file mode 100644 index 0000000..203b355 Binary files /dev/null and b/runs/detect/train4/weights/best.pt differ diff --git a/runs/detect/train4/weights/last.pt b/runs/detect/train4/weights/last.pt new file mode 100644 index 0000000..6d3713e Binary files /dev/null and b/runs/detect/train4/weights/last.pt differ diff --git a/train.py b/train.py index 8635350..3207d76 100644 --- a/train.py +++ b/train.py @@ -1,8 +1,7 @@ from ultralytics import YOLO # Load a model -model = YOLO('yolov8n.pt') # load a pretrained model (recommended for training) +model = YOLO('../../project/runs/detect/train3/weights/best.pt') # load a pretrained model (recommended for training) # Train the model -model.train(data='coco128.yaml', epochs=100, imgsz=640) - +model.train(data='../datasets/zcxv.v2i.yolov8/data.yaml', device="cuda")