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308 lines
16 KiB
Python
308 lines
16 KiB
Python
3 weeks ago
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# Ultralytics YOLOv5 🚀, AGPL-3.0 license
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"""
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Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
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Usage - sources:
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$ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam
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img.jpg # image
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vid.mp4 # video
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screen # screenshot
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path/ # directory
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list.txt # list of images
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list.streams # list of streams
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'path/*.jpg' # glob
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'https://youtu.be/LNwODJXcvt4' # YouTube
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'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
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Usage - formats:
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$ python segment/predict.py --weights yolov5s-seg.pt # PyTorch
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yolov5s-seg.torchscript # TorchScript
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yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
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yolov5s-seg_openvino_model # OpenVINO
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yolov5s-seg.engine # TensorRT
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yolov5s-seg.mlmodel # CoreML (macOS-only)
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yolov5s-seg_saved_model # TensorFlow SavedModel
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yolov5s-seg.pb # TensorFlow GraphDef
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yolov5s-seg.tflite # TensorFlow Lite
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yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
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yolov5s-seg_paddle_model # PaddlePaddle
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"""
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import argparse
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import os
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import platform
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import sys
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from pathlib import Path
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import torch
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FILE = Path(__file__).resolve()
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ROOT = FILE.parents[1] # YOLOv5 root directory
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if str(ROOT) not in sys.path:
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sys.path.append(str(ROOT)) # add ROOT to PATH
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ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
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from ultralytics.utils.plotting import Annotator, colors, save_one_box
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from models.common import DetectMultiBackend
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from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
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from utils.general import (
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LOGGER,
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Profile,
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check_file,
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check_img_size,
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check_imshow,
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check_requirements,
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colorstr,
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cv2,
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increment_path,
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non_max_suppression,
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print_args,
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scale_boxes,
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scale_segments,
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strip_optimizer,
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)
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from utils.segment.general import masks2segments, process_mask, process_mask_native
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from utils.torch_utils import select_device, smart_inference_mode
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@smart_inference_mode()
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def run(
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weights=ROOT / "yolov5s-seg.pt", # model.pt path(s)
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source=ROOT / "data/images", # file/dir/URL/glob/screen/0(webcam)
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data=ROOT / "data/coco128.yaml", # dataset.yaml path
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imgsz=(640, 640), # inference size (height, width)
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conf_thres=0.25, # confidence threshold
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iou_thres=0.45, # NMS IOU threshold
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max_det=1000, # maximum detections per image
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device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
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view_img=False, # show results
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save_txt=False, # save results to *.txt
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save_conf=False, # save confidences in --save-txt labels
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save_crop=False, # save cropped prediction boxes
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nosave=False, # do not save images/videos
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classes=None, # filter by class: --class 0, or --class 0 2 3
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agnostic_nms=False, # class-agnostic NMS
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augment=False, # augmented inference
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visualize=False, # visualize features
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update=False, # update all models
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project=ROOT / "runs/predict-seg", # save results to project/name
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name="exp", # save results to project/name
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exist_ok=False, # existing project/name ok, do not increment
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line_thickness=3, # bounding box thickness (pixels)
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hide_labels=False, # hide labels
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hide_conf=False, # hide confidences
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half=False, # use FP16 half-precision inference
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dnn=False, # use OpenCV DNN for ONNX inference
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vid_stride=1, # video frame-rate stride
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retina_masks=False,
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):
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"""Run YOLOv5 segmentation inference on diverse sources including images, videos, directories, and streams."""
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source = str(source)
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save_img = not nosave and not source.endswith(".txt") # save inference images
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is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
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is_url = source.lower().startswith(("rtsp://", "rtmp://", "http://", "https://"))
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webcam = source.isnumeric() or source.endswith(".streams") or (is_url and not is_file)
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screenshot = source.lower().startswith("screen")
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if is_url and is_file:
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source = check_file(source) # download
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# Directories
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save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
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(save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
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# Load model
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device = select_device(device)
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model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
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stride, names, pt = model.stride, model.names, model.pt
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imgsz = check_img_size(imgsz, s=stride) # check image size
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# Dataloader
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bs = 1 # batch_size
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if webcam:
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view_img = check_imshow(warn=True)
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dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
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bs = len(dataset)
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elif screenshot:
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dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
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else:
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dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
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vid_path, vid_writer = [None] * bs, [None] * bs
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# Run inference
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model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
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seen, windows, dt = 0, [], (Profile(device=device), Profile(device=device), Profile(device=device))
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for path, im, im0s, vid_cap, s in dataset:
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with dt[0]:
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im = torch.from_numpy(im).to(model.device)
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im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
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im /= 255 # 0 - 255 to 0.0 - 1.0
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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# Inference
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with dt[1]:
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visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
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pred, proto = model(im, augment=augment, visualize=visualize)[:2]
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# NMS
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with dt[2]:
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pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32)
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# Second-stage classifier (optional)
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# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
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# Process predictions
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for i, det in enumerate(pred): # per image
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seen += 1
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if webcam: # batch_size >= 1
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p, im0, frame = path[i], im0s[i].copy(), dataset.count
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s += f"{i}: "
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else:
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p, im0, frame = path, im0s.copy(), getattr(dataset, "frame", 0)
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p = Path(p) # to Path
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save_path = str(save_dir / p.name) # im.jpg
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txt_path = str(save_dir / "labels" / p.stem) + ("" if dataset.mode == "image" else f"_{frame}") # im.txt
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s += "{:g}x{:g} ".format(*im.shape[2:]) # print string
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imc = im0.copy() if save_crop else im0 # for save_crop
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annotator = Annotator(im0, line_width=line_thickness, example=str(names))
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if len(det):
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if retina_masks:
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# scale bbox first the crop masks
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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
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masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC
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else:
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masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC
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det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
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# Segments
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if save_txt:
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segments = [
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scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
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for x in reversed(masks2segments(masks))
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]
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# Print results
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for c in det[:, 5].unique():
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n = (det[:, 5] == c).sum() # detections per class
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s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
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# Mask plotting
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annotator.masks(
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masks,
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colors=[colors(x, True) for x in det[:, 5]],
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im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous()
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/ 255
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if retina_masks
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else im[i],
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)
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# Write results
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for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
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if save_txt: # Write to file
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seg = segments[j].reshape(-1) # (n,2) to (n*2)
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line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format
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with open(f"{txt_path}.txt", "a") as f:
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f.write(("%g " * len(line)).rstrip() % line + "\n")
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if save_img or save_crop or view_img: # Add bbox to image
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c = int(cls) # integer class
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label = None if hide_labels else (names[c] if hide_conf else f"{names[c]} {conf:.2f}")
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annotator.box_label(xyxy, label, color=colors(c, True))
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# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
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if save_crop:
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save_one_box(xyxy, imc, file=save_dir / "crops" / names[c] / f"{p.stem}.jpg", BGR=True)
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# Stream results
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im0 = annotator.result()
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if view_img:
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if platform.system() == "Linux" and p not in windows:
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windows.append(p)
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cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
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cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
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cv2.imshow(str(p), im0)
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if cv2.waitKey(1) == ord("q"): # 1 millisecond
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exit()
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# Save results (image with detections)
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if save_img:
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if dataset.mode == "image":
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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if vid_path[i] != save_path: # new video
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vid_path[i] = save_path
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if isinstance(vid_writer[i], cv2.VideoWriter):
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vid_writer[i].release() # release previous video writer
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if vid_cap: # video
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fps = vid_cap.get(cv2.CAP_PROP_FPS)
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w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
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h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
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else: # stream
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fps, w, h = 30, im0.shape[1], im0.shape[0]
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save_path = str(Path(save_path).with_suffix(".mp4")) # force *.mp4 suffix on results videos
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vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (w, h))
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vid_writer[i].write(im0)
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# Print time (inference-only)
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LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
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# Print results
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t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
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LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}" % t)
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if save_txt or save_img:
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s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
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LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
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if update:
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strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
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def parse_opt():
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"""Parses command-line options for YOLOv5 inference including model paths, data sources, inference settings, and
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output preferences.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)")
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parser.add_argument("--source", type=str, default=ROOT / "data/images", help="file/dir/URL/glob/screen/0(webcam)")
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parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="(optional) dataset.yaml path")
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parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w")
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parser.add_argument("--conf-thres", type=float, default=0.25, help="confidence threshold")
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parser.add_argument("--iou-thres", type=float, default=0.45, help="NMS IoU threshold")
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parser.add_argument("--max-det", type=int, default=1000, help="maximum detections per image")
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parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
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parser.add_argument("--view-img", action="store_true", help="show results")
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parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
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parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
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parser.add_argument("--save-crop", action="store_true", help="save cropped prediction boxes")
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parser.add_argument("--nosave", action="store_true", help="do not save images/videos")
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parser.add_argument("--classes", nargs="+", type=int, help="filter by class: --classes 0, or --classes 0 2 3")
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parser.add_argument("--agnostic-nms", action="store_true", help="class-agnostic NMS")
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parser.add_argument("--augment", action="store_true", help="augmented inference")
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parser.add_argument("--visualize", action="store_true", help="visualize features")
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parser.add_argument("--update", action="store_true", help="update all models")
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parser.add_argument("--project", default=ROOT / "runs/predict-seg", help="save results to project/name")
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parser.add_argument("--name", default="exp", help="save results to project/name")
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parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
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parser.add_argument("--line-thickness", default=3, type=int, help="bounding box thickness (pixels)")
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parser.add_argument("--hide-labels", default=False, action="store_true", help="hide labels")
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parser.add_argument("--hide-conf", default=False, action="store_true", help="hide confidences")
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parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
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parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
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parser.add_argument("--vid-stride", type=int, default=1, help="video frame-rate stride")
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parser.add_argument("--retina-masks", action="store_true", help="whether to plot masks in native resolution")
|
||
|
|
opt = parser.parse_args()
|
||
|
|
opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand
|
||
|
|
print_args(vars(opt))
|
||
|
|
return opt
|
||
|
|
|
||
|
|
|
||
|
|
def main(opt):
|
||
|
|
"""Executes YOLOv5 model inference with given options, checking for requirements before launching."""
|
||
|
|
check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
|
||
|
|
run(**vars(opt))
|
||
|
|
|
||
|
|
|
||
|
|
if __name__ == "__main__":
|
||
|
|
opt = parse_opt()
|
||
|
|
main(opt)
|