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infer_stream.py
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infer_stream.py
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import numpy as np
import argparse
import itertools
import random
import time
import torch
import cv2
from dataset.baseobject import DatasetBase
from backbone.basenet import BackboneBase
from PIL import ImageDraw, Image
from config.eval_config import EvalConfig as Config
from bbox import BBox
from model import Model
#from roi.pooler import Pooler
def str2bool(b_str):
if b_str.lower() in ('yes', 'true', 't', 'y', '1'):
return True
elif b_str.lower() in ('no', 'false', 'f', 'n', '0'):
return False
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str, default='voc2007', help='name of dataset')
parser.add_argument('--backbone', type=str, default='resnet101', help='resnet18, resnet50, resnet101')
parser.add_argument('--checkpoint_dir', type=str, default='./checkpoint', help='path to checkpoint')
parser.add_argument('--probability_threshold', type=float, default=0.5, 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('--anchor_smooth_l1_loss_beta', type=float, help='default: {:g}'.format(Config.ANCHOR_SMOOTH_L1_LOSS_BETA))
parser.add_argument('--proposal_smooth_l1_loss_beta', type=float, help='default: {:g}'.format(Config.PROPOSAL_SMOOTH_L1_LOSS_BETA))
parser.add_argument('--batch_size', type=int, help='default: {:g}'.format(Config.BATCH_SIZE))
parser.add_argument('--learning_rate', type=float, help='default: {:g}'.format(Config.LEARNING_RATE))
#parser.add_argument('--momentum', type=float, help='default: {:g}'.format(Config.MOMENTUM))
#parser.add_argument('--weight_decay', type=float, help='default: {:g}'.format(Config.WEIGHT_DECAY))
#parser.add_argument('--step_lr_sizes', type=str, help='default: {!s}'.format(Config.STEP_LR_SIZES))
#parser.add_argument('--step_lr_gamma', type=float, help='default: {:g}'.format(Config.STEP_LR_GAMMA))
#parser.add_argument('--warm_up_factor', type=float, help='default: {:g}'.format(Config.WARM_UP_FACTOR))
#parser.add_argument('--warm_up_num_iters', type=int, help='default: {:d}'.format(Config.WARM_UP_NUM_ITERS))
parser.add_argument('--input', type=str, default='./input/test.jpg', help='path to input image')
parser.add_argument('--output', type=str, default='./output/result.jpg', help='path to output result image')
parser.add_argument('--period', type=int, help='period of inference')
parser.add_argument('--cuda', default=False, type=str2bool)
args = parser.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
def _infer_stream(path_to_input_stream_endpoint: str, period_of_inference: int, dataset_name: str, backbone_name: str, prob_thresh: float):
dataset_class = DatasetBase.from_name(dataset_name)
backbone = BackboneBase.from_name(backbone_name)(pretrained=True)
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).to(device)
model.load(args.checkpoint_dir)
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):
_, frame = video_capture.read()
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_dir', 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('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.input
period_of_inference = args.period
dataset_name = args.dataset
backbone_name = args.backbone
#path_to_checkpoint = args.checkpoint_dir
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, dataset_name, backbone_name, prob_thresh)
#main()