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detect_with_onnx.py
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#!/usr/bin/env python
# -*- coding:utf-8 -*-
import onnxruntime as ort
import argparse
import cv2
import time
import re
import math
import torch.utils.data as data
from config import get_config
from utils.coco import COCODetection, detect_onnx_collate
from utils import timer
from utils.output_utils import nms_numpy, after_nms_numpy, draw_img
from utils.common_utils import ProgressBar
from utils.augmentations import val_aug
from utils.box_utils import make_anchors
parser = argparse.ArgumentParser(description='YOLACT Detection with ONNX.')
parser.add_argument('--weight', default='onnx_files/res101_coco.onnx', type=str)
parser.add_argument('--image', default=None, type=str, help='The folder of images for detecting.')
parser.add_argument('--video', default=None, type=str, help='The path of the video to evaluate.')
parser.add_argument('--img_size', type=int, default=544, help='The image size for validation.')
parser.add_argument('--traditional_nms', default=False, action='store_true', help='Whether to use traditional nms.')
parser.add_argument('--hide_mask', default=False, action='store_true', help='Hide masks in results.')
parser.add_argument('--hide_bbox', default=False, action='store_true', help='Hide boxes in results.')
parser.add_argument('--hide_score', default=False, action='store_true', help='Hide scores in results.')
parser.add_argument('--cutout', default=False, action='store_true', help='Cut out each object and save.')
parser.add_argument('--save_lincomb', default=False, action='store_true', help='Show the generating process of masks.')
parser.add_argument('--no_crop', default=False, action='store_true',
help='Do not crop the output masks with the predicted bounding box.')
parser.add_argument('--real_time', default=False, action='store_true', help='Show the detection results real-timely.')
parser.add_argument('--visual_thre', default=0.3, type=float,
help='Detections with a score under this threshold will be removed.')
args = parser.parse_args()
prefix = re.findall(r'best_\d+\.\d+_', args.weight)[0]
suffix = re.findall(r'_\d+\.pth', args.weight)[0]
args.cfg = args.weight.split(prefix)[-1].split(suffix)[0]
cfg = get_config(args, mode='detect')
sess = ort.InferenceSession(cfg.weight)
input_name = sess.get_inputs()[0].name
anchors = []
fpn_fm_shape = [math.ceil(cfg.img_size / stride) for stride in (8, 16, 32, 64, 128)]
for i, size in enumerate(fpn_fm_shape):
anchors += make_anchors(cfg, size, size, cfg.scales[i])
# detect images
if cfg.image is not None:
dataset = COCODetection(cfg, mode='detect')
data_loader = data.DataLoader(dataset, 1, num_workers=4, shuffle=False,
pin_memory=True, collate_fn=detect_onnx_collate)
ds = len(data_loader)
assert ds > 0, 'No .jpg images found.'
progress_bar = ProgressBar(40, ds)
timer.reset()
for i, (img, img_origin, img_name) in enumerate(data_loader):
if i == 1:
timer.start()
img_h, img_w = img_origin.shape[0:2]
with timer.counter('forward'):
class_p, box_p, coef_p, proto_p = sess.run(None, {input_name: img})
with timer.counter('nms'):
ids_p, class_p, box_p, coef_p, proto_p = nms_numpy(class_p, box_p, coef_p, proto_p, anchors, cfg)
with timer.counter('after_nms'):
ids_p, class_p, boxes_p, masks_p = after_nms_numpy(ids_p, class_p, box_p, coef_p,
proto_p, img_h, img_w, cfg)
with timer.counter('save_img'):
img_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, img_origin, cfg, img_name=img_name)
cv2.imwrite(f'results/onnx_images/{img_name}', img_numpy)
aa = time.perf_counter()
if i > 0:
batch_time = aa - temp
timer.add_batch_time(batch_time)
temp = aa
if i > 0:
t_t, t_d, t_f, t_nms, t_an, t_si = timer.get_times(['batch', 'data', 'forward',
'nms', 'after_nms', 'save_img'])
fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t
bar_str = progress_bar.get_bar(i + 1)
print(f'\rTesting: {bar_str} {i + 1}/{ds}, fps: {fps:.2f} | total fps: {t_fps:.2f} | '
f't_t: {t_t:.3f} | t_d: {t_d:.3f} | t_f: {t_f:.3f} | t_nms: {t_nms:.3f} | '
f't_after_nms: {t_an:.3f} | t_save_img: {t_si:.3f}', end='')
print('\nFinished, saved in: results/onnx_images.')
# detect videos
elif cfg.video is not None:
vid = cv2.VideoCapture(cfg.video)
target_fps = round(vid.get(cv2.CAP_PROP_FPS))
frame_width = round(vid.get(cv2.CAP_PROP_FRAME_WIDTH))
frame_height = round(vid.get(cv2.CAP_PROP_FRAME_HEIGHT))
num_frames = round(vid.get(cv2.CAP_PROP_FRAME_COUNT))
name = cfg.video.split('/')[-1]
video_writer = cv2.VideoWriter(f'results/onnx_videos/{name}', cv2.VideoWriter_fourcc(*"mp4v"), target_fps,
(frame_width, frame_height))
progress_bar = ProgressBar(40, num_frames)
timer.reset()
t_fps = 0
for i in range(num_frames):
if i == 1:
timer.start()
frame_origin = vid.read()[1]
img_h, img_w = frame_origin.shape[0:2]
frame_trans = val_aug(frame_origin, cfg.img_size)[None, :]
with timer.counter('forward'):
class_p, box_p, coef_p, proto_p = sess.run(None, {input_name: frame_trans})
with timer.counter('nms'):
ids_p, class_p, box_p, coef_p, proto_p = nms_numpy(class_p, box_p, coef_p, proto_p, anchors, cfg)
with timer.counter('after_nms'):
ids_p, class_p, boxes_p, masks_p = after_nms_numpy(ids_p, class_p, box_p, coef_p,
proto_p, img_h, img_w, cfg)
with timer.counter('save_img'):
frame_numpy = draw_img(ids_p, class_p, boxes_p, masks_p, frame_origin, cfg, fps=t_fps)
if cfg.real_time:
cv2.imshow('Detection', frame_numpy)
cv2.waitKey(1)
else:
video_writer.write(frame_numpy)
aa = time.perf_counter()
if i > 0:
batch_time = aa - temp
timer.add_batch_time(batch_time)
temp = aa
if i > 0:
t_t, t_d, t_f, t_nms, t_an, t_si = timer.get_times(['batch', 'data', 'forward',
'nms', 'after_nms', 'save_img'])
fps, t_fps = 1 / (t_d + t_f + t_nms + t_an), 1 / t_t
bar_str = progress_bar.get_bar(i + 1)
print(f'\rDetecting: {bar_str} {i + 1}/{num_frames}, fps: {fps:.2f} | total fps: {t_fps:.2f} | '
f't_t: {t_t:.3f} | t_d: {t_d:.3f} | t_f: {t_f:.3f} | t_nms: {t_nms:.3f} | '
f't_after_nms: {t_an:.3f} | t_save_img: {t_si:.3f}', end='')
if not cfg.real_time:
print(f'\n\nFinished, saved in: results/onnx_videos/{name}')
vid.release()
video_writer.release()