infer_stream.py

import argparse
import itertools
import random
import time
import torch

import cv2
import numpy as np
from PIL import ImageDraw, Image

from backbone.base import Base as BackboneBase
from config.eval_config import EvalConfig as Config
from dataset.base import Base as DatasetBase
from bbox import BBox
from model import Model
from roi.pooler import Pooler


def _infer_stream(path_to_input_stream_endpoint: str, period_of_inference: int, path_to_checkpoint: str, dataset_name: str, backbone_name: str, prob_thresh: float):
    dataset_class = DatasetBase.from_name(dataset_name)
    backbone = BackboneBase.from_name(backbone_name)(pretrained=False)
    model = Model(backbone, dataset_class.num_classes(), pooler_mode=Config.POOLER_MODE,
                  anchor_ratios=Config.ANCHOR_RATIOS, anchor_sizes=Config.ANCHOR_SIZES,
                  rpn_pre_nms_top_n=Config.RPN_PRE_NMS_TOP_N, rpn_post_nms_top_n=Config.RPN_POST_NMS_TOP_N).cuda()
    model.load(path_to_checkpoint)

    if path_to_input_stream_endpoint.isdigit():
        path_to_input_stream_endpoint = int(path_to_input_stream_endpoint)
    video_capture = cv2.VideoCapture(path_to_input_stream_endpoint)

    with torch.no_grad():
        for sn in itertools.count(start=1):
            success, frame = video_capture.read()
            
            if not success:
                break

            if sn % period_of_inference != 0:
                continue

            timestamp = time.time()

            image = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
            image = Image.fromarray(image)
            image_tensor, scale = dataset_class.preprocess(image, Config.IMAGE_MIN_SIDE, Config.IMAGE_MAX_SIDE)

            detection_bboxes, detection_classes, detection_probs, _ = \
                model.eval().forward(image_tensor.unsqueeze(dim=0).cuda())
            detection_bboxes /= scale

            kept_indices = detection_probs > prob_thresh
            detection_bboxes = detection_bboxes[kept_indices]
            detection_classes = detection_classes[kept_indices]
            detection_probs = detection_probs[kept_indices]

            draw = ImageDraw.Draw(image)

            for bbox, cls, prob in zip(detection_bboxes.tolist(), detection_classes.tolist(), detection_probs.tolist()):
                color = random.choice(['red', 'green', 'blue', 'yellow', 'purple', 'white'])
                bbox = BBox(left=bbox[0], top=bbox[1], right=bbox[2], bottom=bbox[3])
                category = dataset_class.LABEL_TO_CATEGORY_DICT[cls]

                draw.rectangle(((bbox.left, bbox.top), (bbox.right, bbox.bottom)), outline=color)
                draw.text((bbox.left, bbox.top), text=f'{category:s} {prob:.3f}', fill=color)

            image = np.array(image)
            frame = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)

            elapse = time.time() - timestamp
            fps = 1 / elapse
            cv2.putText(frame, f'FPS = {fps:.1f}', (20, 20), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 1, cv2.LINE_AA)

            cv2.imshow('easy-faster-rcnn-pytorch', frame)
            if cv2.waitKey(10) == 27:
                break

    video_capture.release()
    cv2.destroyAllWindows()


if __name__ == '__main__':
    def main():
        parser = argparse.ArgumentParser()
        parser.add_argument('-s', '--dataset', type=str, choices=DatasetBase.OPTIONS, required=True, help='name of dataset')
        parser.add_argument('-b', '--backbone', type=str, choices=BackboneBase.OPTIONS, required=True, help='name of backbone model')
        parser.add_argument('-c', '--checkpoint', type=str, required=True, help='path to checkpoint')
        parser.add_argument('-p', '--probability_threshold', type=float, default=0.6, help='threshold of detection probability')
        parser.add_argument('--image_min_side', type=float, help='default: {:g}'.format(Config.IMAGE_MIN_SIDE))
        parser.add_argument('--image_max_side', type=float, help='default: {:g}'.format(Config.IMAGE_MAX_SIDE))
        parser.add_argument('--anchor_ratios', type=str, help='default: "{!s}"'.format(Config.ANCHOR_RATIOS))
        parser.add_argument('--anchor_sizes', type=str, help='default: "{!s}"'.format(Config.ANCHOR_SIZES))
        parser.add_argument('--pooler_mode', type=str, choices=Pooler.OPTIONS, help='default: {.value:s}'.format(Config.POOLER_MODE))
        parser.add_argument('--rpn_pre_nms_top_n', type=int, help='default: {:d}'.format(Config.RPN_PRE_NMS_TOP_N))
        parser.add_argument('--rpn_post_nms_top_n', type=int, help='default: {:d}'.format(Config.RPN_POST_NMS_TOP_N))
        parser.add_argument('--stream_input', type=str, help='path to input stream endpoint')
        parser.add_argument('--period', type=int, help='period of inference')
        args = parser.parse_args()

        path_to_input_stream_endpoint = args.stream_input
        period_of_inference = args.period
        dataset_name = args.dataset
        backbone_name = args.backbone
        path_to_checkpoint = args.checkpoint
        prob_thresh = args.probability_threshold

        Config.setup(image_min_side=args.image_min_side, image_max_side=args.image_max_side,
                     anchor_ratios=args.anchor_ratios, anchor_sizes=args.anchor_sizes, pooler_mode=args.pooler_mode,
                     rpn_pre_nms_top_n=args.rpn_pre_nms_top_n, rpn_post_nms_top_n=args.rpn_post_nms_top_n)

        print('Arguments:')
        for k, v in vars(args).items():
            print(f'\t{k} = {v}')
        print(Config.describe())

        _infer_stream(path_to_input_stream_endpoint, period_of_inference, path_to_checkpoint, dataset_name, backbone_name, prob_thresh)

    main()