使用nohup后台运行python，print没有输出到日志
nohup python foobar.py > foobar.log 2>&1 &
发现foobar.log中显示不出来python程序中print的东西。
这是因为python的输出有缓冲，导致foobar.log并不能够马上看到输出。
python 有个-u参数，使得python不启用缓冲。
nohup python -u foobar.py > foobar.log 2>&1 &
其他玩法:
只输出错误到日志
# Only error write to log
nohup python -u ./foobar.py> /dev/null 2>foobar.log &
不输出到日志
# Nothing to Display
nohup python -u ./foobar.py> /dev/null 2>&1 &
全部print输出到日志
# Write all to log
nohup python -u ./foobar.py> ./foobar.log 2>&1 &
https://blog.csdn.net/Statham_stone/article/details/78290813
PostgreSql 习惯上会将特殊数据类型的各个节点按字典/JSON类型存储
程序中需要获得完整的数据信息的时候，需要对这个节点进行自展开。
以下使用global id方式进行展开，一般适用于SQL+NoSQL结合的系统使用
import sys, os
import numpy as np
def get_object_by_gid(id):
for dict in data["json"]:
if dict["gid"] == id:
return dict.copy()
def self_exact_node(key):
dict = get_object_by_gid(key)
for k,v in dict.items():
if k == "sub_item" :
item_arr = []
for id in v["gids"]:
item_arr.append(self_exact_node(id))
v["item_arr"] = item_arr.copy()
return dict
def demo():
data_exact = data.copy()
for d in data_exact["json"]:
d = self_exact_node(d["gid"])
def main():
demo()
if __name__ == '__main__’:
sys.exit(main()) |
import sys, os
import numpy as np
def get_object_by_gid(id):
for dict in data["json"]:
if dict["gid"] == id:
return dict.copy()
def self_exact_node(key):
dict = get_object_by_gid(key)
for k,v in dict.items():
if k == "sub_item" :
item_arr = []
for id in v["gids"]:
item_arr.append(self_exact_node(id))
v["item_arr"] = item_arr.copy()
return dict
def demo():
data_exact = data.copy()
for d in data_exact["json"]:
d = self_exact_node(d["gid"])
def main():
demo()
if __name__ == '__main__’:
sys.exit(main())
其他玩法
# Global ID方式
"gid": "大分类2:中分类2:子分类2",
# 复合ID
"id":{"l1_cat":"大分类1","l2_cat":"中分类2","l3_cat":"子分类2"}
# 链表式
"chain":{"next_gid":"","pre_gid":"","head_gid":"","tail_gid":""}
# 二叉树式
"btree":{"next_sibling":"","child":”"} |
# Global ID方式
"gid": "大分类2:中分类2:子分类2",
# 复合ID
"id":{"l1_cat":"大分类1","l2_cat":"中分类2","l3_cat":"子分类2"}
# 链表式
"chain":{"next_gid":"","pre_gid":"","head_gid":"","tail_gid":""}
# 二叉树式
"btree":{"next_sibling":"","child":”"}
Read more…
使用applyColorMap可以对单个通道的图像进行伪彩色处理和热力图
OpenCV的定义了12种colormap常数，选择一个需要的即可
cv2.applyColorMap(heatmap_g, cv2.COLORMAP_JET)
图像可以使用addWeighted进行叠加处理
cv2.addWeighted(heatmap_img, alpha, merge_img, 1-alpha, 0, merge_img) # 将热度图覆盖到原图
def heatmap_overlay(image,heatmap):
# 灰度化heatmap
heatmap_g = heatmap.astype(np.uint8)
# 热力图伪彩色
heatmap_color = cv2.applyColorMap(heatmap_g, cv2.COLORMAP_JET)
# overlay热力图
merge_img = image.copy()
heatmap_img = heatmap_color.copy()
overlay = image.copy()
alpha = 0.25 # 设置覆盖图片的透明度
#cv2.rectangle(overlay, (0, 0), (merge_img.shape[1], merge_img.shape[0]), (0, 0, 0), -1) # 设置蓝色为热度图基本色
cv2.addWeighted(overlay, alpha, merge_img, 1-alpha, 0, merge_img) # 将背景热度图覆盖到原图
cv2.addWeighted(heatmap_img, alpha, merge_img, 1-alpha, 0, merge_img) # 将热度图覆盖到原图
return merge_img |
def heatmap_overlay(image,heatmap):
# 灰度化heatmap
heatmap_g = heatmap.astype(np.uint8)
# 热力图伪彩色
heatmap_color = cv2.applyColorMap(heatmap_g, cv2.COLORMAP_JET)
# overlay热力图
merge_img = image.copy()
heatmap_img = heatmap_color.copy()
overlay = image.copy()
alpha = 0.25 # 设置覆盖图片的透明度
#cv2.rectangle(overlay, (0, 0), (merge_img.shape[1], merge_img.shape[0]), (0, 0, 0), -1) # 设置蓝色为热度图基本色
cv2.addWeighted(overlay, alpha, merge_img, 1-alpha, 0, merge_img) # 将背景热度图覆盖到原图
cv2.addWeighted(heatmap_img, alpha, merge_img, 1-alpha, 0, merge_img) # 将热度图覆盖到原图
return merge_img
参考：
https://blog.csdn.net/u013381011/article/details/78341861
将视频按FPS拆解成单张图片
使用cv2.VideoCapture
cv2.VideoCapture(video_path)
计算FPS使用，注意部分压缩视频FPS存在丢帧情况，需要进行跳帧处理
fps = int(vidcap.get(cv2.CAP_PROP_FPS))
def video_split():
video_path = 'test/video/video_01.mp4'
video_name = video_path[:-4]
vidcap = cv2.VideoCapture(video_path)
success,image = vidcap.read()
fps = int(vidcap.get(cv2.CAP_PROP_FPS))
count = 0
while success:
image = image_process(image)
cv2.imwrite("%s/%d.jpg" % (video_name, count), image)
#if count % fps == 0:
# cv2.imwrite("%s/%d.jpg" % (video_name, int(count / fps)), image)
print('Process %dth seconds: ' % int(count / fps), success)
success,image = vidcap.read()
count += 1 |
def video_split():
video_path = 'test/video/video_01.mp4'
video_name = video_path[:-4]
vidcap = cv2.VideoCapture(video_path)
success,image = vidcap.read()
fps = int(vidcap.get(cv2.CAP_PROP_FPS))
count = 0
while success:
image = image_process(image)
cv2.imwrite("%s/%d.jpg" % (video_name, count), image)
#if count % fps == 0:
# cv2.imwrite("%s/%d.jpg" % (video_name, int(count / fps)), image)
print('Process %dth seconds: ' % int(count / fps), success)
success,image = vidcap.read()
count += 1
Read more…
对遮罩层进行轮廓检索并合并到图像上
第一步使用高斯模糊GaussianBlur模糊边缘像素
第二步使用Canny侦测边界，丢弃部分散点
最后使用findContours找到外框
#对遮罩层进行轮廓检索并合并到图像上
def drawMaskContoursOverImage(image,mask):
# convert colorspace
gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
#image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
# 3*3 GaussianBlur
# gray = cv2.GaussianBlur(gray, (3, 3), 0)
# canny detect edge
gray = cv2.Canny(gray, 100, 300)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
# binary是最后返回的二值图像
#findContours()第一个参数是源图像、第二个参数是轮廓检索模式，第三个参数是轮廓逼近方法
#输出是轮廓和层次结构，轮廓是图像中所有轮廓的python列表，每个单独的轮廓是对象边界点的(x,y)坐标的Numpy数组
binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(image, contours, -1, (0, 255, 0), 1)
# 写图像
cv2.imwrite('%s.masked.png'%pair[0],image,[int(cv2.IMWRITE_PNG_COMPRESSION),3]) |
#对遮罩层进行轮廓检索并合并到图像上
def drawMaskContoursOverImage(image,mask):
# convert colorspace
gray = cv2.cvtColor(mask, cv2.COLOR_BGR2GRAY)
#image = cv2.cvtColor(image, cv2.COLOR_GRAY2BGR)
# 3*3 GaussianBlur
# gray = cv2.GaussianBlur(gray, (3, 3), 0)
# canny detect edge
gray = cv2.Canny(gray, 100, 300)
ret, thresh = cv2.threshold(gray, 127, 255, cv2.THRESH_BINARY)
# binary是最后返回的二值图像
#findContours()第一个参数是源图像、第二个参数是轮廓检索模式，第三个参数是轮廓逼近方法
#输出是轮廓和层次结构，轮廓是图像中所有轮廓的python列表，每个单独的轮廓是对象边界点的(x,y)坐标的Numpy数组
binary, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
cv2.drawContours(image, contours, -1, (0, 255, 0), 1)
# 写图像
cv2.imwrite('%s.masked.png'%pair[0],image,[int(cv2.IMWRITE_PNG_COMPRESSION),3])
参考
https://blog.csdn.net/dz4543/article/details/80655067
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