ultralytics 8.1.26 LoadImagesAndVideos batched inference (#8817)
Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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Glenn Jocher
GitHub
parent
1f9667fff2
commit
7451ca1f54
11 changed files with 186 additions and 171 deletions
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@ -73,9 +73,7 @@ class BasePredictor:
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data (dict): Data configuration.
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device (torch.device): Device used for prediction.
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dataset (Dataset): Dataset used for prediction.
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vid_path (str): Path to video file.
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vid_writer (cv2.VideoWriter): Video writer for saving video output.
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data_path (str): Path to data.
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vid_writer (dict): Dictionary of {save_path: video_writer, ...} writer for saving video output.
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"""
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def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None):
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@ -100,10 +98,11 @@ class BasePredictor:
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self.imgsz = None
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self.device = None
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self.dataset = None
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self.vid_path, self.vid_writer, self.vid_frame = None, None, None
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self.vid_writer = {} # dict of {save_path: video_writer, ...}
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self.plotted_img = None
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self.data_path = None
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self.source_type = None
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self.seen = 0
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self.windows = []
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self.batch = None
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self.results = None
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self.transforms = None
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@ -155,44 +154,6 @@ class BasePredictor:
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letterbox = LetterBox(self.imgsz, auto=same_shapes and self.model.pt, stride=self.model.stride)
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return [letterbox(image=x) for x in im]
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def write_results(self, idx, results, batch):
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"""Write inference results to a file or directory."""
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p, im, _ = batch
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log_string = ""
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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if self.source_type.webcam or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1
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log_string += f"{idx}: "
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frame = self.dataset.count
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else:
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frame = getattr(self.dataset, "frame", 0)
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self.data_path = p
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self.txt_path = str(self.save_dir / "labels" / p.stem) + ("" if self.dataset.mode == "image" else f"_{frame}")
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log_string += "%gx%g " % im.shape[2:] # print string
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result = results[idx]
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log_string += result.verbose()
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if self.args.save or self.args.show: # Add bbox to image
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plot_args = {
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"line_width": self.args.line_width,
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"boxes": self.args.show_boxes,
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"conf": self.args.show_conf,
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"labels": self.args.show_labels,
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}
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if not self.args.retina_masks:
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plot_args["im_gpu"] = im[idx]
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self.plotted_img = result.plot(**plot_args)
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# Write
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if self.args.save_txt:
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result.save_txt(f"{self.txt_path}.txt", save_conf=self.args.save_conf)
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if self.args.save_crop:
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result.save_crop(
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save_dir=self.save_dir / "crops",
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file_name=self.data_path.stem + ("" if self.dataset.mode == "image" else f"_{frame}"),
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)
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return log_string
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def postprocess(self, preds, img, orig_imgs):
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"""Post-processes predictions for an image and returns them."""
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return preds
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@ -228,18 +189,20 @@ class BasePredictor:
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else None
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)
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self.dataset = load_inference_source(
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source=source, vid_stride=self.args.vid_stride, buffer=self.args.stream_buffer
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source=source,
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batch=self.args.batch,
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vid_stride=self.args.vid_stride,
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buffer=self.args.stream_buffer,
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)
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self.source_type = self.dataset.source_type
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if not getattr(self, "stream", True) and (
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self.dataset.mode == "stream" # streams
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or len(self.dataset) > 1000 # images
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self.source_type.stream
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or self.source_type.screenshot
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or len(self.dataset) > 1000 # many images
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or any(getattr(self.dataset, "video_flag", [False]))
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): # videos
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LOGGER.warning(STREAM_WARNING)
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self.vid_path = [None] * self.dataset.bs
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self.vid_writer = [None] * self.dataset.bs
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self.vid_frame = [None] * self.dataset.bs
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self.vid_writer = {}
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@smart_inference_mode()
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def stream_inference(self, source=None, model=None, *args, **kwargs):
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@ -271,10 +234,9 @@ class BasePredictor:
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ops.Profile(device=self.device),
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)
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self.run_callbacks("on_predict_start")
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for batch in self.dataset:
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for self.batch in self.dataset:
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self.run_callbacks("on_predict_batch_start")
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self.batch = batch
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path, im0s, vid_cap, s = batch
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paths, im0s, is_video, s = self.batch
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# Preprocess
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with profilers[0]:
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@ -290,8 +252,8 @@ class BasePredictor:
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# Postprocess
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with profilers[2]:
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self.results = self.postprocess(preds, im, im0s)
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self.run_callbacks("on_predict_postprocess_end")
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# Visualize, save, write results
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n = len(im0s)
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for i in range(n):
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@ -301,41 +263,32 @@ class BasePredictor:
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"inference": profilers[1].dt * 1e3 / n,
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"postprocess": profilers[2].dt * 1e3 / n,
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}
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p, im0 = path[i], None if self.source_type.tensor else im0s[i].copy()
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p = Path(p)
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if self.args.verbose or self.args.save or self.args.save_txt or self.args.show:
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s += self.write_results(i, self.results, (p, im, im0))
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if self.args.save or self.args.save_txt:
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self.results[i].save_dir = self.save_dir.__str__()
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if self.args.show and self.plotted_img is not None:
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self.show(p)
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if self.args.save and self.plotted_img is not None:
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self.save_preds(vid_cap, i, str(self.save_dir / p.name))
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s[i] += self.write_results(i, Path(paths[i]), im, is_video)
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# Print batch results
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if self.args.verbose:
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LOGGER.info("\n".join(s))
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self.run_callbacks("on_predict_batch_end")
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yield from self.results
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# Print time (inference-only)
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if self.args.verbose:
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LOGGER.info(f"{s}{profilers[1].dt * 1E3:.1f}ms")
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# Release assets
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if isinstance(self.vid_writer[-1], cv2.VideoWriter):
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self.vid_writer[-1].release() # release final video writer
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for v in self.vid_writer.values():
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if isinstance(v, cv2.VideoWriter):
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v.release()
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# Print results
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# Print final results
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if self.args.verbose and self.seen:
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t = tuple(x.t / self.seen * 1e3 for x in profilers) # speeds per image
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LOGGER.info(
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f"Speed: %.1fms preprocess, %.1fms inference, %.1fms postprocess per image at shape "
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f"{(1, 3, *im.shape[2:])}" % t
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f"{(min(self.args.batch, self.seen), 3, *im.shape[2:])}" % t
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)
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if self.args.save or self.args.save_txt or self.args.save_crop:
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nl = len(list(self.save_dir.glob("labels/*.txt"))) # number of labels
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s = f"\n{nl} label{'s' * (nl > 1)} saved to {self.save_dir / 'labels'}" if self.args.save_txt else ""
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LOGGER.info(f"Results saved to {colorstr('bold', self.save_dir)}{s}")
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self.run_callbacks("on_predict_end")
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def setup_model(self, model, verbose=True):
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@ -354,48 +307,81 @@ class BasePredictor:
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self.args.half = self.model.fp16 # update half
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self.model.eval()
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def show(self, p):
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"""Display an image in a window using OpenCV imshow()."""
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im0 = self.plotted_img
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if platform.system() == "Linux" and p not in self.windows:
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self.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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cv2.waitKey(500 if self.batch[3].startswith("image") else 1) # 1 millisecond
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def write_results(self, i, p, im, is_video):
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"""Write inference results to a file or directory."""
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string = "" # print string
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if len(im.shape) == 3:
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im = im[None] # expand for batch dim
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if self.source_type.stream or self.source_type.from_img or self.source_type.tensor: # batch_size >= 1
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string += f"{i}: "
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frame = self.dataset.count
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else:
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frame = getattr(self.dataset, "frame", 0) - len(self.results) + i
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def save_preds(self, vid_cap, idx, save_path):
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self.txt_path = self.save_dir / "labels" / (p.stem + f"_{frame}" if is_video[i] else "")
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string += "%gx%g " % im.shape[2:]
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result = self.results[i]
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result.save_dir = self.save_dir.__str__() # used in other locations
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string += result.verbose() + f"{result.speed['inference']:.1f}ms"
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# Add predictions to image
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if self.args.save or self.args.show:
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self.plotted_img = result.plot(
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line_width=self.args.line_width,
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boxes=self.args.show_boxes,
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conf=self.args.show_conf,
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labels=self.args.show_labels,
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im_gpu=None if self.args.retina_masks else im[i],
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)
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# Save results
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if self.args.save_txt:
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result.save_txt(f"{self.txt_path}.txt", save_conf=self.args.save_conf)
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if self.args.save_crop:
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result.save_crop(save_dir=self.save_dir / "crops", file_name=self.txt_path.stem)
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if self.args.show:
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self.show(str(p), is_video[i])
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if self.args.save:
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self.save_predicted_images(str(self.save_dir / p.name), is_video[i], frame)
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return string
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def save_predicted_images(self, save_path="", is_video=False, frame=0):
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"""Save video predictions as mp4 at specified path."""
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im0 = self.plotted_img
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# Save imgs
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if self.dataset.mode == "image":
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cv2.imwrite(save_path, im0)
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else: # 'video' or 'stream'
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im = self.plotted_img
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# Save videos and streams
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if is_video:
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frames_path = f'{save_path.split(".", 1)[0]}_frames/'
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if self.vid_path[idx] != save_path: # new video
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self.vid_path[idx] = save_path
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if save_path not in self.vid_writer: # new video
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if self.args.save_frames:
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Path(frames_path).mkdir(parents=True, exist_ok=True)
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self.vid_frame[idx] = 0
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if isinstance(self.vid_writer[idx], cv2.VideoWriter):
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self.vid_writer[idx].release() # release previous video writer
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if vid_cap: # video
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fps = int(vid_cap.get(cv2.CAP_PROP_FPS)) # integer required, floats produce error in MP4 codec
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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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suffix, fourcc = (".mp4", "avc1") if MACOS else (".avi", "WMV2") if WINDOWS else (".avi", "MJPG")
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self.vid_writer[idx] = cv2.VideoWriter(
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str(Path(save_path).with_suffix(suffix)), cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)
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self.vid_writer[save_path] = cv2.VideoWriter(
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filename=str(Path(save_path).with_suffix(suffix)),
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fourcc=cv2.VideoWriter_fourcc(*fourcc),
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fps=30, # integer required, floats produce error in MP4 codec
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frameSize=(im.shape[1], im.shape[0]), # (width, height)
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)
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# Write video
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self.vid_writer[idx].write(im0)
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# Write frame
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# Save video
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self.vid_writer[save_path].write(im)
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if self.args.save_frames:
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cv2.imwrite(f"{frames_path}{self.vid_frame[idx]}.jpg", im0)
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self.vid_frame[idx] += 1
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cv2.imwrite(f"{frames_path}{frame}.jpg", im)
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# Save images
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else:
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cv2.imwrite(save_path, im)
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def show(self, p="", is_video=False):
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"""Display an image in a window using OpenCV imshow()."""
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im = self.plotted_img
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if platform.system() == "Linux" and p not in self.windows:
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self.windows.append(p)
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cv2.namedWindow(p, cv2.WINDOW_NORMAL | cv2.WINDOW_KEEPRATIO) # allow window resize (Linux)
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cv2.resizeWindow(p, im.shape[1], im.shape[0]) # (width, height)
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cv2.imshow(p, im)
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cv2.waitKey(1 if is_video else 500) # 1 millisecond
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def run_callbacks(self, event: str):
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"""Runs all registered callbacks for a specific event."""
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