diff --git a/TRAIN_AND_RUN_README.md b/TRAIN_AND_RUN_README.md index 2176a84ef..b86baf434 100644 --- a/TRAIN_AND_RUN_README.md +++ b/TRAIN_AND_RUN_README.md @@ -231,6 +231,8 @@ chmod a-w \ ``` #### Generate Object Predictions in the Scene + +##### YoloV7 Models Note that the input COCO file is that which was generated in the previous step. This is to ensure that all represented videos and image frames are predicted on and present in both COCO files. @@ -247,6 +249,22 @@ python-tpl/yolov7/yolov7/detect_ptg.py \ Additional debug outputs may optionally be generated. See the `-h`/`--help` options for more details. +##### YOLO V11 Models +``` +yolo_v11_inference_objects \ + -i m5_xstat-activity_truth.coco.json \ + -o m5_xstat-object_detections/object_detections-yolov11_baseline_xl_sgd_20241216.coco.json \ + --model-hands ~/dev/darpa-ptg/angel_system/model_files/object_detector/hands_model.pt \ + --hand-img-size 768 \ + --model-objects /data/paul.tunison/data/darpa-ptg/yolo_v11_object_detection/m5_v0.56/runs/detect/baseline-xl/weights/best.pt \ + --obj-img-size 640 \ + -e hands \ + --model-device cuda:0 \ + --conf-thresh 0.307 \ + --save-img m5_xstat-object_detections-v11_images \ + --save-vid +``` + #### Generate Pose Predictions Note that the input COCO file is that which was generated in the `Generate activity classification truth COCO file` section. diff --git a/angel_system/object_detection/yolov11_detect.py b/angel_system/object_detection/yolov11_detect.py new file mode 100644 index 000000000..3825af7f5 --- /dev/null +++ b/angel_system/object_detection/yolov11_detect.py @@ -0,0 +1,443 @@ +#!/usr/bin/env python3 + +from collections import defaultdict +import logging +from pathlib import Path +import random +from typing import Dict +from typing import Optional +from typing import Sequence +import warnings + +import click +import cv2 +import kwcoco +import moviepy.video.io.ImageSequenceClip +import numpy as np +import torch +import ubelt as ub +from ultralytics import YOLO + +from angel_system.object_detection.yolov8_detect import predict_hands + + +LOG = logging.getLogger(__name__) + + +def plot_one_box(xywh, img, color=None, label=None, line_thickness=1) -> None: + """ + Plot one detection box into the given image with CV2. + + Based on the similar function from YOLO v7 plotting code. + + :param xywh: Extent of the box to plot in xywh format, where the xy is the + upper-left coordinate of the box. + :param img: is the image matrix to draw the box into. + :param color: Optional RGB value tuple of the color to draw. + :param label: Optional text label to draw for the box. + :param line_thickness: Thickness of the box lines to draw. + """ + # Plots one bounding box on image img + tl = ( + line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1 + ) # line/font thickness + color = color or [random.randint(0, 255) for _ in range(3)] + c1, c2 = (int(xywh[0]), int(xywh[1])), ( + int(xywh[0] + xywh[2]), + int(xywh[1] + xywh[3]), + ) + cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA) + if label: + tf = max(tl - 1, 1) # font thickness + t_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0] + c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3 + cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA) # filled + cv2.putText( + img, + label, + (c1[0], c1[1] - 2), + 0, + tl / 3, + [225, 255, 255], + thickness=tf, + lineType=cv2.LINE_AA, + ) + + +@click.command() +@click.help_option("-h", "--help") +@click.option( + "-i", + "--input-coco-file", + type=click.Path(exists=True, dir_okay=False, path_type=Path), + help=( + "MS-COCO file specifying image files to perform object detection over. " + "Image and Video sections from this COCO file will be maintained in the " + "output COCO file." + ), + required=True, +) +@click.option( + "--img-root", + type=click.Path(exists=True, file_okay=False, path_type=Path), + default=None, + help=( + "Optional override for the input COCO dataset bundle root. This is " + "necessary when the input COCO file uses relative paths and the COCO " + "file itself is not located in the bundle root directory." + ), +) +@click.option( + "-o", + "--output-coco-file", + type=click.Path(dir_okay=False, path_type=Path), + help="Output COCO file to write object detection results.", + required=True, +) +@click.option( + "--model-hands", + "hand_model_ckpt", + type=click.Path(exists=True, dir_okay=False, path_type=Path), + help="Model checkpoint for the Yolo v8 hand detector.", + required=True, +) +@click.option( + "--model-objects", + "objs_model_ckpt", + type=click.Path(exists=True, dir_okay=False, path_type=Path), + help="Model checkpoint for the Yolo v7 object detector.", + required=True, +) +@click.option( + "-e", + "--exclude-obj-class", + "obj_exclude_classes", + multiple=True, + help=( + "Exclude these object classes from the class list provided by the " + "object model. This is for when the object model was trained with " + "some classes excluded, but YOLO provided the metadata for them " + "anyway." + ), +) +@click.option( + "--model-device", + default="", + help="The CUDA device to use, i.e. '0' or '0,1,2,3' or 'cpu'.", +) +@click.option( + "--obj-img-size", + type=int, + default=640, + help=( + "Data input size for the detection models for objects. This should be " + "a multiple of the model's stride parameter." + ), +) +@click.option( + "--hand-img-size", + type=int, + default=768, + help=( + "Data input size for the detection model for hands. This should be a " + "multiple of the model's stride parameter." + ), +) +@click.option( + "--conf-thresh", + type=float, + default=0.25, + help=( + "Object confidence threshold. Predicted objects with confidence less " + "than this will not be considered for output." + ), +) +@click.option( + "--tensorrt", + is_flag=True, + help=( + "Export object and hand models to TensorRT and use FP16 to " + "accelerate prediction performance." + ), +) +@click.option( + "--save-img", + "save_dir", + type=click.Path(file_okay=False, path_type=Path), + default=None, + help=( + "Optionally enable the plotting of detections back to the image and " + "saving them out to disk, rooted in this directory. Only detections " + "with confidence above our configured threshold will be considered " + "for plotting." + ), +) +@click.option( + "--save-vid", + is_flag=True, + help=( + "Optionally enable the creation of an MP4 video from the images " + "rendered due to --save-img. This option only has an effect if the " + "--save-img option is provided. The video file will be save next to " + "the directory into which component images are saved." + ), +) +@torch.inference_mode() +def yolo_v11_inference_objects( + input_coco_file: Path, + img_root: Optional[Path], + output_coco_file: Path, + hand_model_ckpt: Path, + objs_model_ckpt: Path, + obj_exclude_classes: Sequence[str], + model_device: str, + obj_img_size: int, + hand_img_size: int, + conf_thresh: float, + tensorrt: bool, + save_dir: Optional[Path], + save_vid: bool, +): + """ + Script for use in generating object detection results based on an input + COCO file's video/image specifications. + + Expected use-case: generate object detections for video frames (images) + that we have activity classification truth for. + + \b + Example: + python3 yolo_v11_inference_objects \\ + """ + logging.basicConfig( + level=logging.INFO, + ) + + guiding_dset = kwcoco.CocoDataset(input_coco_file, bundle_dpath=img_root) + + # Prevent overwriting an existing file. These are expensive to compute so + # we don't want to mess that up. + if output_coco_file.is_file(): + raise ValueError( + f"Output COCO file already exists, refusing to overwrite: " + f"{output_coco_file}" + ) + output_coco_file.parent.mkdir(parents=True, exist_ok=True) + dset = kwcoco.CocoDataset() + dset.fpath = output_coco_file.as_posix() + + object_model = YOLO(objs_model_ckpt, task="detect") + LOG.info( + "Loaded object model with classes:\n" + + "\n".join(f'\t- [{n[0]}] "{n[1]}"' for n in object_model.names.items()) + ) + hand_model = YOLO(hand_model_ckpt, task="detect") + LOG.info( + "Loaded hand model with classes:\n" + + "\n".join(f'\t- [{n[0]}] "{n[1]}"' for n in hand_model.names.items()) + ) + + if tensorrt: + LOG.info("Exporting object model to TensorRT") + om_trt_path = object_model.export( + format="engine", + imgsz=obj_img_size, + device=model_device, + half=True, # this was taking a long time to process? + # nms=True, + ) + LOG.info("Loading TensorRT object model") + object_model = YOLO(om_trt_path, task="detect") + LOG.info("Exporting hand model to TensorRT") + hm_trt_path = hand_model.export( + format="engine", + imgsz=hand_img_size, + device=model_device, + half=True, # this was taking a long time to process? + # nms=True, + ) + LOG.info("Loading TensorRT hand model") + hand_model = YOLO(hm_trt_path, task="detect") + + model_half = model_device.lower() != "cpu" + + cls_names = [p[1] for p in sorted(object_model.names.items())] + cls_colors = [[random.randint(0, 255) for _ in range(3)] for _ in cls_names] + + # Port over the videos and images sections from the input dataset to the + # new one. + dset.dataset["videos"] = guiding_dset.dataset["videos"] + dset.dataset["images"] = guiding_dset.dataset["images"] + dset.index.build(dset) + # Equality can later be tested with: + # guiding_dset.index.videos == dset.index.videos + # guiding_dset.index.imgs == dset.index.imgs + + # Add categories + for cls_name in obj_exclude_classes: + if cls_name not in cls_names: + warnings.warn( + f'Requested exclusion of object class named "{cls_name}", ' + f"however this class is not present in the object model." + ) + exclude_set = set(obj_exclude_classes) + for i, object_label in enumerate(cls_names): + if object_label not in exclude_set: + dset.ensure_category(name=object_label, id=i) + else: + LOG.info(f'Excluding object model class: "{object_label}"') + # Inject categories for the hand-model additions. + left_hand_cid = dset.ensure_category(name="hand (left)") + right_hand_cid = dset.ensure_category(name="hand (right)") + hands_cat_to_cid = {"hand (left)": left_hand_cid, "hand (right)": right_hand_cid} + + # model warm-up going into the prediction loop + LOG.info("Warming up models...") + warmup_image = np.random.randint(0, 255, (16, 16, 3), dtype=np.uint8) + object_model( + source=warmup_image, device=model_device, half=model_half, verbose=False + ) + hand_model(source=warmup_image, device=model_device, half=model_half, verbose=False) + LOG.info("Warming up models... Done") + + # ------------------------------------------------------------------------- + # Generate object/hand predictions + + # Mapping of video_id to the filepath for which component frames have been + # written out to. + video_image_outputs: Dict[int, Dict[int, str]] = defaultdict(dict) + # Dictionary of image output directories per video ID + video_id_to_frame_dir: Dict[int, Path] = dict() + + object_predict_kwargs = dict( + conf=conf_thresh, + device=model_device, + nms=True, + half=model_half, + verbose=False, + ) + if obj_img_size is not None: + object_predict_kwargs["imgsz"] = obj_img_size + + hand_predict_kwargs = dict( + hand_model=hand_model, + device=model_device, + nms=True, + half=model_half, + ) + if hand_img_size is not None: + hand_predict_kwargs["imgsz"] = hand_img_size + + for img_id in ub.ProgIter( + dset.images(), + desc="Processing Images", + verbose=3, + ): + img_path: Path = dset.get_image_fpath(img_id) + img0 = cv2.imread(img_path.as_posix()) + if img0 is None: + raise RuntimeError(f"Failed to read image file: {img_path}") + + # returns list of length=num images, which is always 1 here. + object_preds = object_model.predict(source=img0, **object_predict_kwargs)[0] + + hand_boxes, hand_labels, hand_confs = predict_hands( + img0=img0, + **hand_predict_kwargs, + ) + + # YOLO xywh output defines the xy as the cetner point, not the + # upper-left as required by the COCO format, thus take the xyxy output + # and subtracting out the upper-left. + obj_box_xywh = object_preds.boxes.xyxy.cpu() + obj_box_xywh[:, 2:] -= obj_box_xywh[:, :2] + obj_box_areas = torch.multiply(obj_box_xywh[:, 2], obj_box_xywh[:, 3]) + for box_xywh, box_cls, box_conf, box_area in zip( + obj_box_xywh.tolist(), + object_preds.boxes.cls.to(int).tolist(), + object_preds.boxes.conf.tolist(), + obj_box_areas.tolist(), + ): + dset.add_annotation( + image_id=img_id, + category_id=box_cls, + bbox=box_xywh, + score=box_conf, + area=box_area, + ) + if save_dir is not None: + plot_one_box( + box_xywh, + img0, + color=cls_colors[box_cls], + label=f"{cls_names[box_cls]} {box_conf:.2f}", + ) + + # Convert hand box XYXY coordinates into XYWH where XY is the + # upper-left. + hand_boxes_xywh = np.asarray(hand_boxes).reshape(-1, 4) + hand_boxes_xywh[:, 2:] -= hand_boxes_xywh[:, :2] + hand_areas = np.multiply(hand_boxes_xywh[:, 2], hand_boxes_xywh[:, 3]) + for box_xywh, box_lbl, box_conf, box_area in zip( + hand_boxes_xywh.tolist(), + hand_labels, + hand_confs, + hand_areas, + ): + box_cls = hands_cat_to_cid[box_lbl] + dset.add_annotation( + image_id=img_id, + category_id=box_cls, + bbox=box_xywh, + score=box_conf, + area=box_area, + ) + if save_dir is not None: + plot_one_box( + box_xywh, + img0, + color=[0, 0, 0], + label=f"{box_lbl} {box_conf:.2f}", + ) + + # Optionally draw object detection results to an image. + # If we want to save as a video, also save the paths so we can create + # the video after detecting everything. + if save_dir is not None: + vid_id = dset.index.imgs[img_id]["video_id"] + if vid_id not in video_id_to_frame_dir: + vid_obj = dset.index.videos[vid_id] + save_imgs_dir = save_dir / Path(vid_obj["name"]).stem + save_imgs_dir.mkdir(parents=True, exist_ok=True) + video_id_to_frame_dir[vid_id] = save_imgs_dir + save_path = (video_id_to_frame_dir[vid_id] / img_path.name).as_posix() + if not cv2.imwrite(save_path, img0): + raise RuntimeError(f"Failed to write debug image: {save_path}") + img_obj = dset.index.imgs[img_id] + video_image_outputs[vid_id][img_obj["frame_index"]] = save_path + + # If configured, create and save videos of debug images for each video + # effectively processed. + if save_dir and save_vid: + for vid_id, frame_set in ub.ProgIter( + video_image_outputs.items(), + desc="Creating Videos", + verbose=3, + ): + frame_set: Dict[int, str] + vid_obj = dset.index.videos[vid_id] + video_save_path = save_dir / f"{Path(vid_obj['name']).stem}-objects.mp4" + vid_frames = [p[0] for p in sorted(frame_set.items())] + clip = moviepy.video.io.ImageSequenceClip.ImageSequenceClip( + vid_frames, fps=vid_obj["framerate"] + ) + clip.write_videofile(video_save_path.as_posix()) + LOG.info(f"Saved video to: {video_save_path}") + + LOG.info(f"Saving output COCO file... ({output_coco_file})") + dset.dump(dset.fpath, newlines=True) + LOG.info(f"Saved output COCO file: {output_coco_file}") + + +if __name__ == "__main__": + yolo_v11_inference_objects() diff --git a/angel_system/object_detection/yolov8_detect.py b/angel_system/object_detection/yolov8_detect.py index c2e676952..d8719f856 100644 --- a/angel_system/object_detection/yolov8_detect.py +++ b/angel_system/object_detection/yolov8_detect.py @@ -1,15 +1,30 @@ -import numpy as np +from typing import Optional +import numpy as np from ultralytics import YOLO as YOLOv8 -def predict_hands(hand_model: YOLOv8, img0: np.array, device: str, imgsz: int) -> tuple: +def predict_hands( + hand_model: YOLOv8, + img0: np.array, + device: str, + **kwargs, +) -> tuple: """Predict hands using a YOLOv8 hand model and update the labels to be hand(left) and hand(right) + + Boxes returned are in xyxy format (upper-left, lower-right). + + Any additional keyword arguments provided will be passed to the + `YOLO.predict` method. """ width, height = img0.shape[:2] hands_preds = hand_model.predict( - source=img0, conf=0.1, imgsz=imgsz, device=device, verbose=False + conf=0.1, + **kwargs, + source=img0, + device=device, + verbose=False, )[ 0 ] # list of length=num images diff --git a/poetry.lock b/poetry.lock index b33199011..cc007d7ac 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.4 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.7.1 and should not be changed by hand. 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000000000..08593593b --- /dev/null +++ b/scripts/make_orig_fixed.bash @@ -0,0 +1,8 @@ +FPATH="$1" + +FPATH_ORIG="${FPATH}-ORIGINAL" +FPATH_FIXD="${FPATH}-FIXED" + +mv "${FPATH}" "${FPATH_ORIG}" +cp "${FPATH_ORIG}" "${FPATH_FIXD}" +ln -s "$(basename "${FPATH_FIXD}")" "${FPATH}" diff --git a/scripts/remap_yolo_truth_txt_ids.py b/scripts/remap_yolo_truth_txt_ids.py new file mode 100755 index 000000000..b6cdacc5f --- /dev/null +++ b/scripts/remap_yolo_truth_txt_ids.py @@ -0,0 +1,87 @@ +#!/usr/bin/env python3 +from collections import defaultdict +import os +from pathlib import Path +import re +from typing import Dict +from typing import List +import warnings + +import click +from tqdm import tqdm + + +# Regex to capture the `class_id` integer for a line as well as the `remainder` +# for remapping. +RE_TRUTH_LINE = re.compile(r"^(?P\d+)\s+(?P.*)$") + + +@click.command() +@click.help_option("-h", "--help") +@click.argument( + "ROOT_DIR", type=click.Path(exists=True, file_okay=False, path_type=Path) +) +@click.option( + "-r", + "--remap", + "remap_tuple", + nargs=2, + multiple=True, + type=int, + help="Remap class IDs in discovered txt files.", +) +def main(root_dir, remap_tuple): + """ + Remap class IDs in YOLO truth text files from the given values to some new + values. + + **All** class IDs should have a from and to value, even if it is the same + value. + If we encounter any truth files with a class ID that is **not** declared in + an input mapping, we will error. + + \b + Positional Arguments: + ROOT_PATH: + Directory under which we will look for .txt files to modify. + """ + remap = {p[0]: p[1] for p in remap_tuple} + + # scan for all files to modify + truth_path_list = [] + for dirpath, dirnames, filenames in os.walk(root_dir): + for fname in filenames: + if os.path.splitext(fname)[1] == ".txt": + truth_path_list.append(Path(dirpath) / fname) + + new_truth_lines: Dict[Path, List[str]] = defaultdict(list) + + # Stage modified content for each file picked up. + # We don't want to write, or overwrite any files until we know we can + # transform everything in a valid way. + for truth_path in tqdm(truth_path_list, desc="Parsing Truth Files", unit="files"): + with open(truth_path) as f: + for line in f: + m = RE_TRUTH_LINE.match(line) + if m is None: + raise RuntimeError( + f"Encountered issue parsing line in truth file " + f"{truth_path}: encountered line: {line}" + ) + md = m.groupdict() + new_class_id = remap[int(md["class_id"])] + new_truth_lines[truth_path].append(f"{new_class_id} {md['remainder']}") + if truth_path not in new_truth_lines: + warnings.warn(f"No lines in truth file: {truth_path}") + + # We are here if all files have successfully mapped content. Write out\ + # content to the original file locations. + for fpath, new_lines in tqdm( + new_truth_lines.items(), desc="Writing files back out", unit="files" + ): + with open(fpath, "w") as f: + f.writelines(l + "\n" for l in new_lines) + + +if __name__ == "__main__": + main()