deepin-ocr/3rdparty/ncnn/python/examples/yolov4.py
wangzhengyang 718c41634f feat: 切换后端至PaddleOCR-NCNN,切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法,已通过基本的兼容性测试
2.工程改为使用CMake组织,后续为了更好地兼容第三方库,不再提供QMake工程
3.重整权利声明文件,重整代码工程,确保最小化侵权风险

Log: 切换后端至PaddleOCR-NCNN,切换工程为CMake
Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
2022-05-10 10:22:11 +08:00

54 lines
1.7 KiB
Python

# Tencent is pleased to support the open source community by making ncnn available.
#
# Copyright (C) 2020 THL A29 Limited, a Tencent company. All rights reserved.
#
# Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
# in compliance with the License. You may obtain a copy of the License at
#
# https://opensource.org/licenses/BSD-3-Clause
#
# 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 sys
import cv2
import numpy as np
import ncnn
from ncnn.model_zoo import get_model
from ncnn.utils import draw_detection_objects
if __name__ == "__main__":
if len(sys.argv) != 2:
print("Usage: %s [v4l input device or image]\n" % (sys.argv[0]))
sys.exit(0)
devicepath = sys.argv[1]
net = get_model("yolov4_tiny", num_threads=4, use_gpu=True)
# net = get_model("yolov4", num_threads=4, use_gpu=True)
if devicepath.find("/dev/video") == -1:
m = cv2.imread(devicepath)
if m is None:
print("cv2.imread %s failed\n" % (devicepath))
sys.exit(0)
objects = net(m)
draw_detection_objects(m, net.class_names, objects)
else:
cap = cv2.VideoCapture(devicepath)
if cap.isOpened() == False:
print("Failed to open %s" % (devicepath))
sys.exit(0)
while True:
ret, frame = cap.read()
objects = net(frame)
draw_detection_objects(frame, net.class_names, objects)