Merge 2a6c565bee into bdc0518584
Elizarov Ilia
4 weeks ago
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""" |
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The benchmark for trackers in the "tracking" module of the "opencv-contrib" |
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repository. For evaluations used testing set of the LaSOT dataset: |
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https://cis.temple.edu/lasot/ |
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For evaluations used metrics from LaSOT and TrackingNet papers. |
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Link to LaSOT paper: https://arxiv.org/abs/1809.07845 |
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Link to TrackingNet paper: https://arxiv.org/abs/1803.10794 |
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""" |
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|
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import numpy as np |
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import cv2 as cv |
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import argparse |
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import os |
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import sys |
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|
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path = cv.samples.findFile('samples/dnn/dasiamrpn_tracker.py') |
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sys.path.append(os.path.dirname(path)) |
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from dasiamrpn_tracker import DaSiamRPNTracker |
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|
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def get_iou(new, gt): |
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''' |
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During the calculation of intersection over union, we are checking |
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numerical value of area_of_overlap, because if it is equal to 0, |
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we have no intersection. |
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''' |
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new_xmin, new_ymin, new_w, new_h = new |
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gt_xmin, gt_ymin, gt_w, gt_h = gt |
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def get_max_coord(coord, size): return coord + size - 1.0 |
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new_xmax, new_ymax = get_max_coord(new_xmin, new_w), get_max_coord( |
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new_ymin, new_h) |
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gt_xmax, gt_ymax = get_max_coord(gt_xmin, gt_w), get_max_coord( |
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gt_ymin, gt_h) |
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dx = max(0, min(new_xmax, gt_xmax) - max(new_xmin, gt_xmin)) |
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dy = max(0, min(new_ymax, gt_ymax) - max(new_ymin, gt_ymin)) |
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area_of_overlap = dx * dy |
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area_of_union = (new_xmax - new_xmin) * (new_ymax - new_ymin) + ( |
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gt_xmax - gt_xmin) * (gt_ymax - gt_ymin) - area_of_overlap |
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iou = area_of_overlap / area_of_union if area_of_union != 0 else 0 |
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return iou |
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|
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|
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def get_pr(new, gt, is_norm): |
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''' |
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In calculations of precision and normalized precision are used thresholds |
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from original TrackingNet paper. If third argument is "True" - we calculate |
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normalized precision, if it is "False" - we calculate precision without |
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normalization |
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''' |
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new_x, new_y, new_w, new_h = new |
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gt_x, gt_y, gt_w, gt_h = gt |
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def get_center(coord, size): return coord + (size + 1.0) / 2 |
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new_cx, new_cy, gt_cx, gt_cy = get_center(new_x, new_w), get_center( |
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new_y, new_h), get_center(gt_x, gt_w), get_center(gt_y, gt_h) |
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dx = new_cx - gt_cx |
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dy = new_cy - gt_cy |
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if is_norm: |
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dx /= gt_w |
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dy /= gt_h |
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return np.sqrt(dx ** 2 + dy ** 2) |
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|
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|
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def init_tracker(tracker_name): |
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''' |
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Method used for initializing of trackers by creating it |
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via cv.TrackerX_create(). |
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Input: string with tracker name. |
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Output: dictionary 'config' |
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Dictionary 'config' contains trackers names |
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as keys and tuple with call method and number of frames for |
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reinitialization as values. |
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For evaluation of the DaSiamRPN tracker ONNX models should be placed |
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in the same folder with Python script and benchmark |
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''' |
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config = {"Boosting": (cv.TrackerBoosting_create(), 1000), |
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"MIL": (cv.TrackerMIL_create(), 1000), |
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"KCF": (cv.TrackerKCF_create(), 1000), |
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"MedianFlow": (cv.TrackerMedianFlow_create(), 1000), |
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"GOTURN": (cv.TrackerGOTURN_create(), 250), |
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"MOSSE": (cv.TrackerMOSSE_create(), 1000), |
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"CSRT": (cv.TrackerCSRT_create(), 1000), |
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"DaSiamRPN": (DaSiamRPNTracker(), 250)} |
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return config[tracker_name] |
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|
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|
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def main(): |
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parser = argparse.ArgumentParser( |
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description="Run LaSOT-based benchmark for visual object trackers") |
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# As a default argument used name of the original dataset folder |
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parser.add_argument("--dataset", type=str, |
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default="LaSOTTesting", help="Full path to LaSOT") |
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parser.add_argument("-v", dest="visualization", action='store_true', |
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help="Showing process of tracking") |
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args = parser.parse_args() |
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|
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# Creating list of names of the videos via reading names from the txt file |
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video_names = os.path.join(args.dataset, "testing_set.txt") |
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with open(video_names, 'rt') as f: |
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list_of_videos = f.read().rstrip('\n').split('\n') |
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trackers = [ |
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'Boosting', 'MIL', 'KCF', 'MedianFlow',\ |
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'GOTURN', 'MOSSE', 'CSRT', 'DaSiamRPN'] |
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|
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iou_avg = [] |
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pr_avg = [] |
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n_pr_avg = [] |
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|
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# Loop for every tracker |
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for tracker_name in trackers: |
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print("Tracker name: ", tracker_name) |
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number_of_thresholds = 21 |
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iou_video = np.zeros(number_of_thresholds) |
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pr_video = np.zeros(number_of_thresholds) |
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n_pr_video = np.zeros(number_of_thresholds) |
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iou_thr = np.linspace(0, 1, number_of_thresholds) |
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pr_thr = np.linspace(0, 50, number_of_thresholds) |
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n_pr_thr = np.linspace(0, 0.5, number_of_thresholds) |
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|
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# Loop for every video |
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for video_name in list_of_videos: |
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tracker, frames_for_reinit = init_tracker(tracker_name) |
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init_once = False |
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print("\tVideo name: " + str(video_name)) |
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# Open specific video and read ground truth for it |
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gt_file = open(os.path.join(args.dataset, video_name, |
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"groundtruth.txt"), "r") |
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gt_bb = gt_file.readline().rstrip("\n").split(",") |
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init_bb = tuple([float(b) for b in gt_bb]) |
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video_sequence = sorted(os.listdir(os.path.join( |
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args.dataset, video_name, "img"))) |
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iou_values = [] |
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pr_values = [] |
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n_pr_values = [] |
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frame_counter = len(video_sequence) |
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# Loop for every frame in the video |
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for number_of_the_frame, image in enumerate(video_sequence): |
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frame = cv.imread(os.path.join( |
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args.dataset, video_name, "img", image)) |
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gt_bb = tuple([float(x) for x in gt_bb]) |
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# Check for presence of the object on the image |
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# Image is ignored if no object on it |
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if gt_bb[2] == 0 or gt_bb[3] == 0: |
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gt_bb = gt_file.readline().rstrip("\n").split(",") |
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frame_counter -= 1 |
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continue |
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# Condition for reinitialization of the tracker |
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if ((number_of_the_frame + 1) % frames_for_reinit == 0): |
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tracker, frames_for_reinit = init_tracker(tracker_name) |
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init_once = False |
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init_bb = gt_bb |
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if not init_once: |
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init_state = tracker.init(frame, init_bb) |
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init_once = True |
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init_state, new_bb = tracker.update(frame) |
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if args.visualization: |
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new_x, new_y, new_w, new_h = list(map(int, new_bb)) |
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cv.rectangle(frame, (new_x, new_y), (( |
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new_x + new_w), (new_y + new_h)), (200, 0, 0)) |
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cv.imshow("Tracking", frame) |
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cv.waitKey(1) |
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iou_values.append(get_iou(new_bb, gt_bb)) |
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pr_values.append(get_pr(new_bb, gt_bb, is_norm=False)) |
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n_pr_values.append(get_pr(new_bb, gt_bb, is_norm=True)) |
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|
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# Setting as ground truth bounding box of the next frame |
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gt_bb = gt_file.readline().rstrip("\n").split(",") |
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# Calculating mean arithmetic value for the specific video |
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iou_video += (np.fromiter([sum( |
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i >= thr for i in iou_values).astype( |
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float) / frame_counter for thr in iou_thr], dtype=float)) |
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pr_video += (np.fromiter([sum( |
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i <= thr for i in pr_values).astype( |
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float) / frame_counter for thr in pr_thr], dtype=float)) |
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n_pr_video += (np.fromiter([sum( |
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i <= thr for i in n_pr_values).astype( |
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float) / frame_counter for thr in n_pr_thr], dtype=float)) |
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iou_mean_avg = np.array(iou_video) / len(list_of_videos) |
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pr_mean_avg = np.array(pr_video) / len(list_of_videos) |
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n_pr_mean_avg = np.array(n_pr_video) / len(list_of_videos) |
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# We find the area under the curve according to the trapezoid rule |
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# and normalize by the maximum threshold value |
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iou = np.trapz(iou_mean_avg, x=iou_thr) / iou_thr[-1] |
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pr = np.trapz(pr_mean_avg, x=pr_thr) / pr_thr[-1] |
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n_pr = np.trapz(n_pr_mean_avg, x=n_pr_thr) / n_pr_thr[-1] |
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iou_avg.append('%.4f' % iou) |
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pr_avg.append('%.4f' % pr) |
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n_pr_avg.append('%.4f' % n_pr) |
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titles = ["Names:", "IoU:", "Precision:", "N.Precision:"] |
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data = [titles] + list(zip(trackers, iou_avg, pr_avg, n_pr_avg)) |
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for number, for_tracker in enumerate(data): |
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line = '|'.join(str(x).ljust(20) for x in for_tracker) |
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print(line) |
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if number == 0: |
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print('-' * len(line)) |
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if __name__ == '__main__': |
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main() |
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