285 lines
15 KiB
Python
285 lines
15 KiB
Python
|
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
|
||
|
"""
|
||
|
Run YOLOv5 segmentation inference on images, videos, directories, streams, etc.
|
||
|
|
||
|
Usage - sources:
|
||
|
$ python segment/predict.py --weights yolov5s-seg.pt --source 0 # webcam
|
||
|
img.jpg # image
|
||
|
vid.mp4 # video
|
||
|
screen # screenshot
|
||
|
path/ # directory
|
||
|
list.txt # list of images
|
||
|
list.streams # list of streams
|
||
|
'path/*.jpg' # glob
|
||
|
'https://youtu.be/Zgi9g1ksQHc' # YouTube
|
||
|
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
|
||
|
|
||
|
Usage - formats:
|
||
|
$ python segment/predict.py --weights yolov5s-seg.pt # PyTorch
|
||
|
yolov5s-seg.torchscript # TorchScript
|
||
|
yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
|
||
|
yolov5s-seg_openvino_model # OpenVINO
|
||
|
yolov5s-seg.engine # TensorRT
|
||
|
yolov5s-seg.mlmodel # CoreML (macOS-only)
|
||
|
yolov5s-seg_saved_model # TensorFlow SavedModel
|
||
|
yolov5s-seg.pb # TensorFlow GraphDef
|
||
|
yolov5s-seg.tflite # TensorFlow Lite
|
||
|
yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
|
||
|
yolov5s-seg_paddle_model # PaddlePaddle
|
||
|
"""
|
||
|
|
||
|
import argparse
|
||
|
import os
|
||
|
import platform
|
||
|
import sys
|
||
|
from pathlib import Path
|
||
|
|
||
|
import torch
|
||
|
|
||
|
FILE = Path(__file__).resolve()
|
||
|
ROOT = FILE.parents[1] # YOLOv5 root directory
|
||
|
if str(ROOT) not in sys.path:
|
||
|
sys.path.append(str(ROOT)) # add ROOT to PATH
|
||
|
ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
|
||
|
|
||
|
from models.common import DetectMultiBackend
|
||
|
from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadScreenshots, LoadStreams
|
||
|
from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
|
||
|
increment_path, non_max_suppression, print_args, scale_boxes, scale_segments,
|
||
|
strip_optimizer)
|
||
|
from utils.plots import Annotator, colors, save_one_box
|
||
|
from utils.segment.general import masks2segments, process_mask, process_mask_native
|
||
|
from utils.torch_utils import select_device, smart_inference_mode
|
||
|
|
||
|
|
||
|
@smart_inference_mode()
|
||
|
def run(
|
||
|
weights=ROOT / 'yolov5s-seg.pt', # model.pt path(s)
|
||
|
source=ROOT / 'data/images', # file/dir/URL/glob/screen/0(webcam)
|
||
|
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
|
||
|
imgsz=(640, 640), # inference size (height, width)
|
||
|
conf_thres=0.25, # confidence threshold
|
||
|
iou_thres=0.45, # NMS IOU threshold
|
||
|
max_det=1000, # maximum detections per image
|
||
|
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
|
||
|
view_img=False, # show results
|
||
|
save_txt=False, # save results to *.txt
|
||
|
save_conf=False, # save confidences in --save-txt labels
|
||
|
save_crop=False, # save cropped prediction boxes
|
||
|
nosave=False, # do not save images/videos
|
||
|
classes=None, # filter by class: --class 0, or --class 0 2 3
|
||
|
agnostic_nms=False, # class-agnostic NMS
|
||
|
augment=False, # augmented inference
|
||
|
visualize=False, # visualize features
|
||
|
update=False, # update all models
|
||
|
project=ROOT / 'runs/predict-seg', # save results to project/name
|
||
|
name='exp', # save results to project/name
|
||
|
exist_ok=False, # existing project/name ok, do not increment
|
||
|
line_thickness=3, # bounding box thickness (pixels)
|
||
|
hide_labels=False, # hide labels
|
||
|
hide_conf=False, # hide confidences
|
||
|
half=False, # use FP16 half-precision inference
|
||
|
dnn=False, # use OpenCV DNN for ONNX inference
|
||
|
vid_stride=1, # video frame-rate stride
|
||
|
retina_masks=False,
|
||
|
):
|
||
|
source = str(source)
|
||
|
save_img = not nosave and not source.endswith('.txt') # save inference images
|
||
|
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
|
||
|
is_url = source.lower().startswith(('rtsp://', 'rtmp://', 'http://', 'https://'))
|
||
|
webcam = source.isnumeric() or source.endswith('.streams') or (is_url and not is_file)
|
||
|
screenshot = source.lower().startswith('screen')
|
||
|
if is_url and is_file:
|
||
|
source = check_file(source) # download
|
||
|
|
||
|
# Directories
|
||
|
save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
|
||
|
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
|
||
|
|
||
|
# Load model
|
||
|
device = select_device(device)
|
||
|
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
|
||
|
stride, names, pt = model.stride, model.names, model.pt
|
||
|
imgsz = check_img_size(imgsz, s=stride) # check image size
|
||
|
|
||
|
# Dataloader
|
||
|
bs = 1 # batch_size
|
||
|
if webcam:
|
||
|
view_img = check_imshow(warn=True)
|
||
|
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
|
||
|
bs = len(dataset)
|
||
|
elif screenshot:
|
||
|
dataset = LoadScreenshots(source, img_size=imgsz, stride=stride, auto=pt)
|
||
|
else:
|
||
|
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt, vid_stride=vid_stride)
|
||
|
vid_path, vid_writer = [None] * bs, [None] * bs
|
||
|
|
||
|
# Run inference
|
||
|
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz)) # warmup
|
||
|
seen, windows, dt = 0, [], (Profile(), Profile(), Profile())
|
||
|
for path, im, im0s, vid_cap, s in dataset:
|
||
|
with dt[0]:
|
||
|
im = torch.from_numpy(im).to(model.device)
|
||
|
im = im.half() if model.fp16 else im.float() # uint8 to fp16/32
|
||
|
im /= 255 # 0 - 255 to 0.0 - 1.0
|
||
|
if len(im.shape) == 3:
|
||
|
im = im[None] # expand for batch dim
|
||
|
|
||
|
# Inference
|
||
|
with dt[1]:
|
||
|
visualize = increment_path(save_dir / Path(path).stem, mkdir=True) if visualize else False
|
||
|
pred, proto = model(im, augment=augment, visualize=visualize)[:2]
|
||
|
|
||
|
# NMS
|
||
|
with dt[2]:
|
||
|
pred = non_max_suppression(pred, conf_thres, iou_thres, classes, agnostic_nms, max_det=max_det, nm=32)
|
||
|
|
||
|
# Second-stage classifier (optional)
|
||
|
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
|
||
|
|
||
|
# Process predictions
|
||
|
for i, det in enumerate(pred): # per image
|
||
|
seen += 1
|
||
|
if webcam: # batch_size >= 1
|
||
|
p, im0, frame = path[i], im0s[i].copy(), dataset.count
|
||
|
s += f'{i}: '
|
||
|
else:
|
||
|
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
|
||
|
|
||
|
p = Path(p) # to Path
|
||
|
save_path = str(save_dir / p.name) # im.jpg
|
||
|
txt_path = str(save_dir / 'labels' / p.stem) + ('' if dataset.mode == 'image' else f'_{frame}') # im.txt
|
||
|
s += '%gx%g ' % im.shape[2:] # print string
|
||
|
imc = im0.copy() if save_crop else im0 # for save_crop
|
||
|
annotator = Annotator(im0, line_width=line_thickness, example=str(names))
|
||
|
if len(det):
|
||
|
if retina_masks:
|
||
|
# scale bbox first the crop masks
|
||
|
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
|
||
|
masks = process_mask_native(proto[i], det[:, 6:], det[:, :4], im0.shape[:2]) # HWC
|
||
|
else:
|
||
|
masks = process_mask(proto[i], det[:, 6:], det[:, :4], im.shape[2:], upsample=True) # HWC
|
||
|
det[:, :4] = scale_boxes(im.shape[2:], det[:, :4], im0.shape).round() # rescale boxes to im0 size
|
||
|
|
||
|
# Segments
|
||
|
if save_txt:
|
||
|
segments = [
|
||
|
scale_segments(im0.shape if retina_masks else im.shape[2:], x, im0.shape, normalize=True)
|
||
|
for x in reversed(masks2segments(masks))]
|
||
|
|
||
|
# Print results
|
||
|
for c in det[:, 5].unique():
|
||
|
n = (det[:, 5] == c).sum() # detections per class
|
||
|
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
|
||
|
|
||
|
# Mask plotting
|
||
|
annotator.masks(
|
||
|
masks,
|
||
|
colors=[colors(x, True) for x in det[:, 5]],
|
||
|
im_gpu=torch.as_tensor(im0, dtype=torch.float16).to(device).permute(2, 0, 1).flip(0).contiguous() /
|
||
|
255 if retina_masks else im[i])
|
||
|
|
||
|
# Write results
|
||
|
for j, (*xyxy, conf, cls) in enumerate(reversed(det[:, :6])):
|
||
|
if save_txt: # Write to file
|
||
|
seg = segments[j].reshape(-1) # (n,2) to (n*2)
|
||
|
line = (cls, *seg, conf) if save_conf else (cls, *seg) # label format
|
||
|
with open(f'{txt_path}.txt', 'a') as f:
|
||
|
f.write(('%g ' * len(line)).rstrip() % line + '\n')
|
||
|
|
||
|
if save_img or save_crop or view_img: # Add bbox to image
|
||
|
c = int(cls) # integer class
|
||
|
label = None if hide_labels else (names[c] if hide_conf else f'{names[c]} {conf:.2f}')
|
||
|
annotator.box_label(xyxy, label, color=colors(c, True))
|
||
|
# annotator.draw.polygon(segments[j], outline=colors(c, True), width=3)
|
||
|
if save_crop:
|
||
|
save_one_box(xyxy, imc, file=save_dir / 'crops' / names[c] / f'{p.stem}.jpg', BGR=True)
|
||
|
|
||
|
# Stream results
|
||
|
im0 = annotator.result()
|
||
|
if view_img:
|
||
|
if platform.system() == 'Linux' and p not in windows:
|
||
|
windows.append(p)
|
||
|
cv2.namedWindow(str(p), cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
|
||
|
cv2.resizeWindow(str(p), im0.shape[1], im0.shape[0])
|
||
|
cv2.imshow(str(p), im0)
|
||
|
if cv2.waitKey(1) == ord('q'): # 1 millisecond
|
||
|
exit()
|
||
|
|
||
|
# Save results (image with detections)
|
||
|
if save_img:
|
||
|
if dataset.mode == 'image':
|
||
|
cv2.imwrite(save_path, im0)
|
||
|
else: # 'video' or 'stream'
|
||
|
if vid_path[i] != save_path: # new video
|
||
|
vid_path[i] = save_path
|
||
|
if isinstance(vid_writer[i], cv2.VideoWriter):
|
||
|
vid_writer[i].release() # release previous video writer
|
||
|
if vid_cap: # video
|
||
|
fps = vid_cap.get(cv2.CAP_PROP_FPS)
|
||
|
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
|
||
|
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
|
||
|
else: # stream
|
||
|
fps, w, h = 30, im0.shape[1], im0.shape[0]
|
||
|
save_path = str(Path(save_path).with_suffix('.mp4')) # force *.mp4 suffix on results videos
|
||
|
vid_writer[i] = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps, (w, h))
|
||
|
vid_writer[i].write(im0)
|
||
|
|
||
|
# Print time (inference-only)
|
||
|
LOGGER.info(f"{s}{'' if len(det) else '(no detections), '}{dt[1].dt * 1E3:.1f}ms")
|
||
|
|
||
|
# Print results
|
||
|
t = tuple(x.t / seen * 1E3 for x in dt) # speeds per image
|
||
|
LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}' % t)
|
||
|
if save_txt or save_img:
|
||
|
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
|
||
|
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
|
||
|
if update:
|
||
|
strip_optimizer(weights[0]) # update model (to fix SourceChangeWarning)
|
||
|
|
||
|
|
||
|
def parse_opt():
|
||
|
parser = argparse.ArgumentParser()
|
||
|
parser.add_argument('--weights', nargs='+', type=str, default=ROOT / 'yolov5s-seg.pt', help='model path(s)')
|
||
|
parser.add_argument('--source', type=str, default=ROOT / 'data/images', help='file/dir/URL/glob/screen/0(webcam)')
|
||
|
parser.add_argument('--data', type=str, default=ROOT / 'data/coco128.yaml', help='(optional) dataset.yaml path')
|
||
|
parser.add_argument('--imgsz', '--img', '--img-size', nargs='+', type=int, default=[640], help='inference size h,w')
|
||
|
parser.add_argument('--conf-thres', type=float, default=0.25, help='confidence threshold')
|
||
|
parser.add_argument('--iou-thres', type=float, default=0.45, help='NMS IoU threshold')
|
||
|
parser.add_argument('--max-det', type=int, default=1000, help='maximum detections per image')
|
||
|
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
|
||
|
parser.add_argument('--view-img', action='store_true', help='show results')
|
||
|
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
|
||
|
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
|
||
|
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
|
||
|
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
|
||
|
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --classes 0, or --classes 0 2 3')
|
||
|
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
|
||
|
parser.add_argument('--augment', action='store_true', help='augmented inference')
|
||
|
parser.add_argument('--visualize', action='store_true', help='visualize features')
|
||
|
parser.add_argument('--update', action='store_true', help='update all models')
|
||
|
parser.add_argument('--project', default=ROOT / 'runs/predict-seg', help='save results to project/name')
|
||
|
parser.add_argument('--name', default='exp', help='save results to project/name')
|
||
|
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
|
||
|
parser.add_argument('--line-thickness', default=3, type=int, help='bounding box thickness (pixels)')
|
||
|
parser.add_argument('--hide-labels', default=False, action='store_true', help='hide labels')
|
||
|
parser.add_argument('--hide-conf', default=False, action='store_true', help='hide confidences')
|
||
|
parser.add_argument('--half', action='store_true', help='use FP16 half-precision inference')
|
||
|
parser.add_argument('--dnn', action='store_true', help='use OpenCV DNN for ONNX inference')
|
||
|
parser.add_argument('--vid-stride', type=int, default=1, help='video frame-rate stride')
|
||
|
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):
|
||
|
check_requirements(exclude=('tensorboard', 'thop'))
|
||
|
run(**vars(opt))
|
||
|
|
||
|
|
||
|
if __name__ == "__main__":
|
||
|
opt = parse_opt()
|
||
|
main(opt)
|