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[func add] add vid_stride argument #118

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[func add] add vid_stride argument #118

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133eed0
add vid_stride argument
mucunwuxian Jul 12, 2023
e2db698
adjust demo.py
mucunwuxian Jul 12, 2023
40d478b
re-adjust demo.py
mucunwuxian Jul 12, 2023
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49 changes: 21 additions & 28 deletions tools/demo.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
import cv2
import numpy as np
import torch
from tqdm import tqdm
from loguru import logger
from PIL import Image

Expand All @@ -23,7 +24,6 @@

class Infer():
def __init__(self, config, infer_size=[640,640], device='cuda', output_dir='./', ckpt=None, end2end=False):

self.ckpt_path = ckpt
suffix = ckpt.split('.')[-1]
if suffix == 'onnx':
Expand Down Expand Up @@ -66,9 +66,7 @@ def _pad_image(self, img, target_size):

return ImageList(pad_imgs, img_sizes, pad_sizes)


def _build_engine(self, config, engine_type):

print(f'Inference with {engine_type} engine!')
if engine_type == 'torch':
model = build_local_model(config, self.device)
Expand All @@ -88,7 +86,6 @@ def _build_engine(self, config, engine_type):
return model

def build_tensorRT_engine(self, trt_path):

import tensorrt as trt
from cuda import cuda
loggert = trt.Logger(trt.Logger.INFO)
Expand Down Expand Up @@ -145,11 +142,7 @@ def predict(batch): # result gets copied into output

return predict




def build_onnx_engine(self, onnx_path):

import onnxruntime

session = onnxruntime.InferenceSession(onnx_path)
Expand All @@ -166,7 +159,6 @@ def build_onnx_engine(self, onnx_path):


def preprocess(self, origin_img):

img = transform_img(origin_img, 0,
**self.config.test.augment.transform,
infer_size=self.infer_size)
Expand All @@ -178,10 +170,8 @@ def preprocess(self, origin_img):
return img, (ow, oh)

def postprocess(self, preds, image, origin_shape=None):

if self.engine_type == 'torch':
output = preds

elif self.engine_type == 'onnx':
scores = torch.Tensor(preds[0])
bboxes = torch.Tensor(preds[1])
Expand Down Expand Up @@ -225,9 +215,7 @@ def postprocess(self, preds, image, origin_shape=None):

return bboxes, scores, cls_inds


def forward(self, origin_image):

image, origin_shape = self.preprocess(origin_image)

if self.engine_type == 'torch':
Expand Down Expand Up @@ -301,7 +289,10 @@ def make_parser():
default=True,
type=bool,
help='whether save visualization results')

parser.add_argument('--vid_stride',
default=1,
type=int,
help='video frame-rate stride')

return parser

Expand Down Expand Up @@ -334,22 +325,24 @@ def main():
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*"mp4v"),
fps, (int(width), int(height)))
while True:
ret_val, frame = cap.read()
if ret_val:
bboxes, scores, cls_inds = infer_engine.forward(frame)
result_frame = infer_engine.visualize(frame, bboxes, scores, cls_inds, conf=args.conf, save_result=False)
if args.save_result:
vid_writer.write(result_frame)
for i in tqdm(range(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)))):
# ret_val, frame = cap.read()
cap.grab() # .read() = .grab() followed by .retrieve()
if i % args.vid_stride == 0:
ret_val, frame = cap.retrieve()
if ret_val:
bboxes, scores, cls_inds = infer_engine.forward(frame)
result_frame = infer_engine.visualize(frame, bboxes, scores, cls_inds, conf=args.conf, save_result=False)
if args.save_result:
vid_writer.write(result_frame)
else:
cv2.namedWindow("DAMO-YOLO", cv2.WINDOW_NORMAL)
cv2.imshow("DAMO-YOLO", result_frame)
ch = cv2.waitKey(1)
if ch == 27 or ch == ord("q") or ch == ord("Q"):
break
else:
cv2.namedWindow("DAMO-YOLO", cv2.WINDOW_NORMAL)
cv2.imshow("DAMO-YOLO", result_frame)
ch = cv2.waitKey(1)
if ch == 27 or ch == ord("q") or ch == ord("Q"):
break
else:
break



if __name__ == '__main__':
Expand Down