clip_object_tracker.py

import argparse
import time
from pathlib import Path

import clip

import cv2
import torch
import torch.backends.cudnn as cudnn
from numpy import random
import numpy as np

from models.experimental import attempt_load
from utils.datasets import LoadStreams, LoadImages
from utils.general import xyxy2xywh, \
    strip_optimizer, set_logging, increment_path
from utils.plots import plot_one_box
from utils.torch_utils import select_device, time_synchronized
from utils.roboflow import predict_image

# deep sort imports
from deep_sort import preprocessing, nn_matching
from deep_sort.detection import Detection
from deep_sort.tracker import Tracker
from tools import generate_clip_detections as gdet


def update_tracks(tracker, frame_count, save_txt, txt_path, save_img, view_img, colors, im0, gn):
    if len(tracker.tracks):
        print("[Tracks]")

    for track in tracker.tracks:
        if not track.is_confirmed() or track.time_since_update > 1:
            continue
        xyxy = track.to_tlbr()
        class_num = track.class_num
        bbox = xyxy

        if opt.info:
            print("Tracker ID: {}, Class: {}, BBox Coords (xmin, ymin, xmax, ymax): {}".format(
                str(track.track_id), class_num, (int(bbox[0]), int(bbox[1]), int(bbox[2]), int(bbox[3]))))

        if save_txt:  # Write to file
            xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)
                              ) / gn).view(-1).tolist()  # normalized xywh

            with open(txt_path + '.txt', 'a') as f:
                f.write('frame: {}; track: {}; class: {}; bbox: {};\n'.format(frame_count, track.track_id, class_num,
                                                                              *xywh))

        if save_img or view_img:  # Add bbox to image
            label = f'{class_num} {track.track_id}'
            plot_one_box(xyxy, im0, label=label,
                         color=colors[len(class_num)], line_thickness=3)

def save_image(save_img, dataset, save_path, im0, vid_path, vid_writer, vid_cap):
    if save_img:
        if dataset.mode == 'image':
            cv2.imwrite(save_path, im0)
        else:  # 'video'
            if vid_path != save_path:  # new video
                vid_path = save_path
                if isinstance(vid_writer, cv2.VideoWriter):
                    vid_writer.release()  # release previous video writer

                fourcc = 'mp4v'  # output video codec
                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))
                vid_writer = cv2.VideoWriter(
                    save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
            vid_writer.write(im0)

def detect(save_img=False):
    nms_max_overlap = opt.nms_max_overlap
    max_cosine_distance = opt.max_cosine_distance
    nn_budget = opt.nn_budget

    # initialize deep sort
    model_filename = "ViT-B/32"
    device = "cuda" if torch.cuda.is_available() else "cpu"
    model, transform = clip.load(model_filename, device=device)
    encoder = gdet.create_box_encoder(model, transform, batch_size=1, device=device)
    # calculate cosine distance metric
    metric = nn_matching.NearestNeighborDistanceMetric(
        "cosine", max_cosine_distance, nn_budget)
    # initialize tracker
    tracker = Tracker(metric)

    source, weights, view_img, save_txt, imgsz = opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
    webcam = source.isnumeric() or source.endswith('.txt') or source.lower().startswith(
        ('rtsp://', 'rtmp://', 'http://'))

    # Directories
    save_dir = Path(increment_path(Path(opt.project) / opt.name,
                    exist_ok=opt.exist_ok))  # increment run
    (save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True,
                                                          exist_ok=True)  # make dir

    # Initialize
    set_logging()
    device = select_device(opt.device)
    half = device.type != 'cpu'  # half precision only supported on CUDA

    # Set Dataloader
    vid_path, vid_writer = None, None
    if webcam:
        view_img = True
        cudnn.benchmark = True  # set True to speed up constant image size inference
        dataset = LoadStreams(source, img_size=imgsz)
    else:
        save_img = True
        dataset = LoadImages(source, img_size=imgsz)

    # colors
    colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(80)]


    frame_count = 0
    for path, img, im0s, vid_cap in dataset:

        # Roboflow Inference
        t1 = time_synchronized()
        pred, classes = predict_image(vid_cap, opt.api_key, opt.url, frame_count)
        pred = [torch.tensor(pred)]

        img = torch.from_numpy(img).to(device)
        img = img.half() if half else img.float()  # uint8 to fp16/32
        img /= 255.0  # 0 - 255 to 0.0 - 1.0
        if img.ndimension() == 3:
            img = img.unsqueeze(0)

        t2 = time_synchronized()

        # Process detections
        for i, det in enumerate(pred):  # detections per image
            if webcam:  # batch_size >= 1
                p, s, im0, frame = path[i], '%g: ' % i, im0s[i].copy(
                ), dataset.count
            else:
                p, s, im0, frame = path, '', im0s, getattr(dataset, 'frame', 0)

            p = Path(p)  # to Path
            save_path = str(save_dir / p.name)  # img.jpg
            txt_path = str(save_dir / 'labels' / p.stem) + \
                ('' if dataset.mode == 'image' else f'_{frame}')  # img.txt

            # normalization gain whwh
            gn = torch.tensor(im0.shape)[[1, 0, 1, 0]]
            if len(det):

                print("\n[Detections]")
                # Print results
                clss = np.array(classes)
                for c in np.unique(clss):
                    n = (clss == c).sum()  # detections per class
                    s += f'{n} {c}, '  # add to string

                print(s)

                trans_bboxes = det[:, :4].clone()
                bboxes = trans_bboxes[:, :4].cpu()
                confs = det[:, 4]

                # encode yolo detections and feed to tracker
                features = encoder(im0, bboxes)
                detections = [Detection(bbox, conf, class_num, feature) for bbox, conf, class_num, feature in zip(
                    bboxes, confs, classes, features)]

                # run non-maxima supression
                boxs = np.array([d.tlwh for d in detections])
                scores = np.array([d.confidence for d in detections])
                class_nums = np.array([d.class_num for d in detections])
                indices = preprocessing.non_max_suppression(
                    boxs, class_nums, nms_max_overlap, scores)
                detections = [detections[i] for i in indices]

                # Call the tracker
                tracker.predict()
                tracker.update(detections)

                # update tracks
                update_tracks(tracker, frame_count, save_txt, txt_path, save_img, view_img, colors, im0, gn)

            # Print time (inference + NMS)
            print(f'Done. ({t2 - t1:.3f}s)')

            # Stream results
            if view_img:
                cv2.imshow(str(p), im0)
                if cv2.waitKey(1) == ord('q'):  # q to quit
                    raise StopIteration

            # Save results (image with detections)
            save_image(save_img, dataset, save_path, im0, vid_path, vid_writer, vid_cap)

            frame_count = frame_count+1

    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 ''
        print(f"Results saved to {save_dir}{s}")

    print(f'Done. ({time.time() - t0:.3f}s)')


if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--weights', nargs='+', type=str,
                        default='yolov5s.pt', help='model.pt path(s)')
    # file/folder, 0 for webcam
    parser.add_argument('--source', type=str,
                        default='data/images', help='source')
    parser.add_argument('--img-size', type=int, default=640,
                        help='inference size (pixels)')
    parser.add_argument('--conf-thres', type=float,
                        default=0.25, help='object confidence threshold')
    parser.add_argument('--iou-thres', type=float,
                        default=0.45, help='IOU threshold for NMS')
    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='display 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('--classes', nargs='+', type=int,
                        help='filter by class: --class 0, or --class 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('--update', action='store_true',
                        help='update all models')
    parser.add_argument('--project', default='runs/detect',
                        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('--nms_max_overlap', type=float, default=1.0,
                        help='Non-maxima suppression threshold: Maximum detection overlap.')
    parser.add_argument('--max_cosine_distance', type=float, default=0.4,
                        help='Gating threshold for cosine distance metric (object appearance).')
    parser.add_argument('--nn_budget', type=int, default=None,
                        help='Maximum size of the appearance descriptors allery. If None, no budget is enforced.')
    parser.add_argument('--api_key', default=None,
                        help='Roboflow API Key.')
    parser.add_argument('--url', default=None,
                        help='Roboflow Model URL.')
    parser.add_argument('--info', action='store_true',
                        help='Print debugging info.')
    opt = parser.parse_args()
    print(opt)

    with torch.no_grad():
        if opt.update:  # update all models (to fix SourceChangeWarning)
            for opt.weights in ['yolov5s.pt', 'yolov5m.pt', 'yolov5l.pt', 'yolov5x.pt']:
                detect()
                strip_optimizer(opt.weights)
        else:
            detect()