PaddleX/dygraph/paddlex/ppdet/modeling/mot/visualization.py

# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

import cv2
import numpy as np


def tlwhs_to_tlbrs(tlwhs):
    tlbrs = np.copy(tlwhs)
    if len(tlbrs) == 0:
        return tlbrs
    tlbrs[:, 2] += tlwhs[:, 0]
    tlbrs[:, 3] += tlwhs[:, 1]
    return tlbrs


def get_color(idx):
    idx = idx * 3
    color = ((37 * idx) % 255, (17 * idx) % 255, (29 * idx) % 255)
    return color


def resize_image(image, max_size=800):
    if max(image.shape[:2]) > max_size:
        scale = float(max_size) / max(image.shape[:2])
        image = cv2.resize(image, None, fx=scale, fy=scale)
    return image


def plot_tracking(image,
                  tlwhs,
                  obj_ids,
                  scores=None,
                  frame_id=0,
                  fps=0.,
                  ids2=None):
    im = np.ascontiguousarray(np.copy(image))
    im_h, im_w = im.shape[:2]

    top_view = np.zeros([im_w, im_w, 3], dtype=np.uint8) + 255

    text_scale = max(1, image.shape[1] / 1600.)
    text_thickness = 2
    line_thickness = max(1, int(image.shape[1] / 500.))

    radius = max(5, int(im_w / 140.))
    cv2.putText(
        im,
        'frame: %d fps: %.2f num: %d' % (frame_id, fps, len(tlwhs)),
        (0, int(15 * text_scale)),
        cv2.FONT_HERSHEY_PLAIN,
        text_scale, (0, 0, 255),
        thickness=2)

    for i, tlwh in enumerate(tlwhs):
        x1, y1, w, h = tlwh
        intbox = tuple(map(int, (x1, y1, x1 + w, y1 + h)))
        obj_id = int(obj_ids[i])
        id_text = '{}'.format(int(obj_id))
        if ids2 is not None:
            id_text = id_text + ', {}'.format(int(ids2[i]))
        _line_thickness = 1 if obj_id <= 0 else line_thickness
        color = get_color(abs(obj_id))
        cv2.rectangle(
            im, intbox[0:2], intbox[2:4], color=color, thickness=line_thickness)
        cv2.putText(
            im,
            id_text, (intbox[0], intbox[1] + 30),
            cv2.FONT_HERSHEY_PLAIN,
            text_scale, (0, 0, 255),
            thickness=text_thickness)
    return im


def plot_trajectory(image, tlwhs, track_ids):
    image = image.copy()
    for one_tlwhs, track_id in zip(tlwhs, track_ids):
        color = get_color(int(track_id))
        for tlwh in one_tlwhs:
            x1, y1, w, h = tuple(map(int, tlwh))
            cv2.circle(
                image, (int(x1 + 0.5 * w), int(y1 + h)), 2, color, thickness=2)
    return image


def plot_detections(image, tlbrs, scores=None, color=(255, 0, 0), ids=None):
    im = np.copy(image)
    text_scale = max(1, image.shape[1] / 800.)
    thickness = 2 if text_scale > 1.3 else 1
    for i, det in enumerate(tlbrs):
        x1, y1, x2, y2 = np.asarray(det[:4], dtype=np.int)
        if len(det) >= 7:
            label = 'det' if det[5] > 0 else 'trk'
            if ids is not None:
                text = '{}# {:.2f}: {:d}'.format(label, det[6], ids[i])
                cv2.putText(
                    im,
                    text, (x1, y1 + 30),
                    cv2.FONT_HERSHEY_PLAIN,
                    text_scale, (0, 255, 255),
                    thickness=thickness)
            else:
                text = '{}# {:.2f}'.format(label, det[6])

        if scores is not None:
            text = '{:.2f}'.format(scores[i])
            cv2.putText(
                im,
                text, (x1, y1 + 30),
                cv2.FONT_HERSHEY_PLAIN,
                text_scale, (0, 255, 255),
                thickness=thickness)

        cv2.rectangle(im, (x1, y1), (x2, y2), color, 2)
    return im