arg 是变元（即自变量argument）的英文缩写。
arg min 就是使后面这个式子达到最小值时的变量的取值
arg max 就是使后面这个式子达到最大值时的变量的取值
例如 函数F(x,y):
arg min F(x,y)就是指当F(x,y)取得最小值时，变量x,y的取值
arg max F(x,y)就是指当F(x,y)取得最大值时，变量x,y的取值
<em>python</em><em>中</em>%：
1. 求模运算，相当于mod，也就是计算除法的余数，比如5%2就得到1。
2. %还用在<em>python</em>的格式化输出，比如：
a = 'test'
print 'it is a %s' %(a)
打印的结果就是 it is a test...
rt
使用keras<em>中</em>的model.fit函数进行训练时出现错误：ValueError: None values not supported.
错误信息如下：
```
File "C:/Users/Desktop/MNISTpractice/mnist.py", line 93, in
model.fit(x_train,y_train, epochs=2, callbacks=callback_list,validation_data=(x_val,y_val))
File "C:\Anaconda3\lib\site-packages\keras\engine\training.py", line 1575, in fit
self._make_train_function()
File "C:\Anaconda3\lib\site-packages\keras\engine\training.py", line 960, in _make_train_function
loss=self.total_loss)
File "C:\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 87, in wrapper
return func(*args, **kwargs)
File "C:\Anaconda3\lib\site-packages\keras\optimizers.py", line 432, in get_updates
m_t = (self.beta_1 * m) + (1. - self.beta_1) * g
File "C:\Anaconda3\lib\site-packages\tensorflow\<em>python</em>\ops\math_ops.py", line 820, in binary_op_wrapper
y = ops.convert_to_tensor(y, dtype=x.dtype.base_dtype, name="y")
File "C:\Anaconda3\lib\site-packages\tensorflow\<em>python</em>\framework\ops.py", line 639, in convert_to_tensor
as_ref=False)
File "C:\Anaconda3\lib\site-packages\tensorflow\<em>python</em>\framework\ops.py", line 704, in internal_convert_to_tensor
ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref)
File "C:\Anaconda3\lib\site-packages\tensorflow\<em>python</em>\framework\constant_op.py", line 113, in _constant_tensor_conversion_function
return constant(v, dtype=dtype, name=name)
File "C:\Anaconda3\lib\site-packages\tensorflow\<em>python</em>\framework\constant_op.py", line 102, in constant
tensor_util.make_tensor_proto(value, dtype=dtype, shape=shape, verify_shape=verify_shape))
File "C:\Anaconda3\lib\site-packages\tensorflow\<em>python</em>\framework\tensor_util.py", line 360, in make_tensor_proto
raise ValueError("None values not supported.")
ValueError: None values not supported.
```
declare
type emp_record is record(f_name employees.first_name%type,h_date employees.hire_date%type);
v_emp_record emp_record;
cursor c3(dept_id number,j_id varchar2) return emp_record
is
select first_name,hire_date from employees where department_id=dept_id and job_id=j_id;
begin
open c3(j_id=>'AD_VP',dept_id=>90);
loop
fetch c3 into v_emp_record;
if c3%found then
dbms_output.put_line(v_emp_record.f_name||'的雇用日期是：'||v_emp_record.h_date);
else
dbms_output.put_line('已经处理完结果集');
exit;
end if;
end loop;
close c3;
end;
items = []
items.append("apple")
items.append("orange")
items.append("banana")
# FAKE METHOD::
items.amount() # Should return 3
How do I get the number of elements in the list?
转载于:https://stackoverflow.com/questions/1712227/how-to-get-the-number-of-elements-in-a-list-in-<em>python</em>
我用darknetAB https://github.com/AlexeyAB/darknet 编译gpu版本后生成darknet.py文件 然后我也编译了yolo_cpp_dll.sln文件 生成dll文件 然后运行darknet.py文件 不显示图片 异常退出 
百度了这个问题 有人说要换<em>python</em>3.5版本 我也尝试了 但是也是不行 不会显示图片。请问各位大佬到底怎么解决？？急！！！谢谢！！！
```
#!<em>python</em>3
"""
Python 3 wrapper for identifying objects in images
Requires DLL compilation
Both the GPU and no-GPU version should be compiled; the no-GPU version should be renamed "yolo_cpp_dll_nogpu.dll".
On a GPU system, you can force CPU evaluation by any of:
- Set global variable DARKNET_FORCE_CPU to True
- Set environment variable CUDA_VISIBLE_DEVICES to -1
- Set environment variable "FORCE_CPU" to "true"
To use, either run performDetect() after import, or modify the end of this file.
See the docstring of performDetect() for parameters.
Directly viewing or returning bounding-boxed images requires scikit-image to be installed (`pip install scikit-image`)
Original *nix 2.7: https://github.com/pjreddie/darknet/blob/0f110834f4e18b30d5f101bf8f1724c34b7b83db/<em>python</em>/darknet.py
Windows Python 2.7 version: https://github.com/AlexeyAB/darknet/blob/fc496d52bf22a0bb257300d3c79be9cd80e722cb/build/darknet/x64/darknet.py
@author: Philip Kahn
@date: 20180503
"""
#pylint: disable=R, W0401, W0614, W0703
from ctypes import *
import math
import random
import os
def sample(probs):
s = sum(probs)
probs = [a/s for a in probs]
r = random.uniform(0, 1)
for i in range(len(probs)):
r = r - probs[i]
if r < 0:
raise ValueError("ForceCPU")
try:
global DARKNET_FORCE_CPU
if DARKNET_FORCE_CPU:
raise ValueError("ForceCPU")
except NameError:
pass
# print(os.environ.keys())
# print("FORCE_CPU flag undefined, proceeding with GPU")
if not os.path.exists(winGPUdll):
raise ValueError("NoDLL")
lib = CDLL(winGPUdll, RTLD_GLOBAL)
except (KeyError, ValueError):
hasGPU = False
if os.path.exists(winNoGPUdll):
lib = CDLL(winNoGPUdll, RTLD_GLOBAL)
print("Notice: CPU-only mode")
else:
# Try the other way, in case no_gpu was
# compile but not renamed
lib = CDLL(winGPUdll, RTLD_GLOBAL)
print("Environment variables indicated a CPU run, but we didn't find `"+winNoGPUdll+"`. Trying a GPU run anyway.")
else:
lib = CDLL("./libdarknet.so", RTLD_GLOBAL)
lib.network_width.argtypes = [c_void_p]
lib.network_width.restype = c_int
lib.network_height.argtypes = [c_void_p]
lib.network_height.restype = c_int
copy_image_from_bytes = lib.copy_image_from_bytes
copy_image_from_bytes.argtypes = [IMAGE,c_char_p]
def network_width(net):
return lib.network_width(net)
def network_height(net):
return lib.network_height(net)
predict = lib.network_predict_ptr
predict.argtypes = [c_void_p, POINTER(c_float)]
predict.restype = POINTER(c_float)
if hasGPU:
set_gpu = lib.cuda_set_device
set_gpu.argtypes = [c_int]
make_image = lib.make_image
make_image.argtypes = [c_int, c_int, c_int]
make_image.restype = IMAGE
get_network_boxes = lib.get_network_boxes
get_network_boxes.argtypes = [c_void_p, c_int, c_int, c_float, c_float, POINTER(c_int), c_int, POINTER(c_int), c_int]
get_network_boxes.restype = POINTER(DETECTION)
make_network_boxes = lib.make_network_boxes
make_network_boxes.argtypes = [c_void_p]
make_network_boxes.restype = POINTER(DETECTION)
free_detections = lib.free_detections
free_detections.argtypes = [POINTER(DETECTION), c_int]
free_ptrs = lib.free_ptrs
free_ptrs.argtypes = [POINTER(c_void_p), c_int]
network_predict = lib.network_predict_ptr
network_predict.argtypes = [c_void_p, POINTER(c_float)]
reset_rnn = lib.reset_rnn
reset_rnn.argtypes = [c_void_p]
load_net = lib.load_network
load_net.argtypes = [c_char_p, c_char_p, c_int]
load_net.restype = c_void_p
load_net_custom = lib.load_network_custom
load_net_custom.argtypes = [c_char_p, c_char_p, c_int, c_int]
load_net_custom.restype = c_void_p
do_nms_obj = lib.do_nms_obj
do_nms_obj.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
do_nms_sort = lib.do_nms_sort
do_nms_sort.argtypes = [POINTER(DETECTION), c_int, c_int, c_float]
free_image = lib.free_image
free_image.argtypes = [IMAGE]
letterbox_image = lib.letterbox_image
letterbox_image.argtypes = [IMAGE, c_int, c_int]
letterbox_image.restype = IMAGE
load_meta = lib.get_metadata
lib.get_metadata.argtypes = [c_char_p]
lib.get_metadata.restype = METADATA
load_image = lib.load_image_color
load_image.argtypes = [c_char_p, c_int, c_int]
load_image.restype = IMAGE
rgbgr_image = lib.rgbgr_image
rgbgr_image.argtypes = [IMAGE]
predict_image = lib.network_predict_image
predict_image.argtypes = [c_void_p, IMAGE]
predict_image.restype = POINTER(c_float)
predict_image_letterbox = lib.network_predict_image_letterbox
predict_image_letterbox.argtypes = [c_void_p, IMAGE]
predict_image_letterbox.restype = POINTER(c_float)
def array_to_image(arr):
import numpy as np
# need to return old values to avoid <em>python</em> freeing memory
arr = arr.transpose(2,0,1)
c = arr.shape[0]
h = arr.shape[1]
w = arr.shape[2]
arr = np.ascontiguousarray(arr.flat, dtype=np.float32) / 255.0
data = arr.ctypes.data_as(POINTER(c_float))
im = IMAGE(w,h,c,data)
return im, arr
def classify(net, meta, im):
out = predict_image(net, im)
res = []
for i in range(meta.classes):
if altNames is None:
nameTag = meta.names[i]
else:
nameTag = altNames[i]
res.append((nameTag, out[i]))
res = sorted(res, key=lambda x: -x[1])
return res
def detect(net, meta, image, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
"""
Performs the meat of the detection
"""
#pylint: disable= C0321
im = load_image(image, 0, 0)
if debug: print("Loaded image")
ret = detect_image(net, meta, im, thresh, hier_thresh, nms, debug)
free_image(im)
if debug: print("freed image")
return ret
def detect_image(net, meta, im, thresh=.5, hier_thresh=.5, nms=.45, debug= False):
#import cv2
#custom_image_bgr = cv2.imread(image) # use: detect(,,imagePath,)
#custom_image = cv2.cvtColor(custom_image_bgr, cv2.COLOR_BGR2RGB)
#custom_image = cv2.resize(custom_image,(lib.network_width(net), lib.network_height(net)), interpolation = cv2.INTER_LINEAR)
#import scipy.misc
#custom_image = scipy.misc.imread(image)
#im, arr = array_to_image(custom_image) # you should comment line below: free_image(im)
num = c_int(0)
if debug: print("Assigned num")
pnum = pointer(num)
if debug: print("Assigned pnum")
predict_image(net, im)
letter_box = 0
#predict_image_letterbox(net, im)
#letter_box = 1
if debug: print("did prediction")
# dets = get_network_boxes(net, custom_image_bgr.shape[1], custom_image_bgr.shape[0], thresh, hier_thresh, None, 0, pnum, letter_box) # OpenCV
dets = get_network_boxes(net, im.w, im.h, thresh, hier_thresh, None, 0, pnum, letter_box)
if debug: print("Got dets")
num = pnum[0]
if debug: print("got zeroth index of pnum")
if nms:
do_nms_sort(dets, num, meta.classes, nms)
if debug: print("did sort")
res = []
if debug: print("about to range")
for j in range(num):
if debug: print("Ranging on "+str(j)+" of "+str(num))
if debug: print("Classes: "+str(meta), meta.classes, meta.names)
for i in range(meta.classes):
if debug: print("Class-ranging on "+str(i)+" of "+str(meta.classes)+"= "+str(dets[j].prob[i]))
if dets[j].prob[i] > 0:
b = dets[j].bbox
if altNames is None:
nameTag = meta.names[i]
else:
nameTag = altNames[i]
if debug:
print("Got bbox", b)
print(nameTag)
print(dets[j].prob[i])
print((b.x, b.y, b.w, b.h))
res.append((nameTag, dets[j].prob[i], (b.x, b.y, b.w, b.h)))
if debug: print("did range")
res = sorted(res, key=lambda x: -x[1])
if debug: print("did sort")
free_detections(dets, num)
if debug: print("freed detections")
return res
netMain = None
metaMain = None
altNames = None
def performDetect(imagePath="data/dog.jpg", thresh= 0.25, configPath = "./cfg/yolov3.cfg", weightPath = "yolov3.weights", metaPath= "./cfg/coco.data", showImage= True, makeImageOnly = False, initOnly= False):
"""
Convenience function to handle the detection and returns of objects.
Displaying bounding boxes requires libraries scikit-image and numpy
Parameters
----------------
imagePath: str
Path to the image to evaluate. Raises ValueError if not found
thresh: float (default= 0.25)
The detection threshold
configPath: str
Path to the configuration file. Raises ValueError if not found
weightPath: str
Path to the weights file. Raises ValueError if not found
metaPath: str
Path to the data file. Raises ValueError if not found
showImage: bool (default= True)
Compute (and show) bounding boxes. Changes return.
makeImageOnly: bool (default= False)
If showImage is True, this won't actually *show* the image, but will create the array and return it.
initOnly: bool (default= False)
Only initialize globals. Don't actually run a prediction.
Returns
----------------------
When showImage is False, list of tuples like
('obj_label', confidence, (bounding_box_x_px, bounding_box_y_px, bounding_box_width_px, bounding_box_height_px))
The X and Y coordinates are from the center of the bounding box. Subtract half the width or height to get the lower corner.
Otherwise, a dict with
{
"detections": as above
"image": a numpy array representing an image, compatible with scikit-image
"caption": an image caption
}
"""
# Import the global variables. This lets us instance Darknet once, then just call performDetect() again without instancing again
global metaMain, netMain, altNames #pylint: disable=W0603
assert 0 < thresh < 1, "Threshold should be a float between zero and one (non-inclusive)"
if not os.path.exists(configPath):
raise ValueError("Invalid config path `"+os.path.abspath(configPath)+"`")
if not os.path.exists(weightPath):
raise ValueError("Invalid weight path `"+os.path.abspath(weightPath)+"`")
if not os.path.exists(metaPath):
raise ValueError("Invalid data file path `"+os.path.abspath(metaPath)+"`")
if netMain is None:
netMain = load_net_custom(configPath.encode("ascii"), weightPath.encode("ascii"), 0, 1) # batch size = 1
if metaMain is None:
metaMain = load_meta(metaPath.encode("ascii"))
if altNames is None:
# In Python 3, the metafile default access craps out on Windows (but not Linux)
# Read the names file and create a list to feed to detect
try:
with open(metaPath) as metaFH:
metaContents = metaFH.read()
import re
match = re.search("names *= *(.*)$", metaContents, re.IGNORECASE | re.MULTILINE)
if match:
result = match.group(1)
else:
result = None
try:
if os.path.exists(result):
with open(result) as namesFH:
namesList = namesFH.read().strip().split("\n")
altNames = [x.strip() for x in namesList]
except TypeError:
pass
except Exception:
pass
if initOnly:
print("Initialized detector")
return None
if not os.path.exists(imagePath):
raise ValueError("Invalid image path `"+os.path.abspath(imagePath)+"`")
# Do the detection
#detections = detect(netMain, metaMain, imagePath, thresh) # if is used cv2.imread(image)
detections = detect(netMain, metaMain, imagePath.encode("ascii"), thresh)
if showImage:
try:
from skimage import io, draw
import numpy as np
image = io.imread(imagePath)
print("*** "+str(len(detections))+" Results, color coded by confidence ***")
imcaption = []
for detection in detections:
label = detection[0]
confidence = detection[1]
pstring = label+": "+str(np.rint(100 * confidence))+"%"
imcaption.append(pstring)
print(pstring)
bounds = detection[2]
shape = image.shape
# x = shape[1]
# xExtent = int(x * bounds[2] / 100)
# y = shape[0]
# yExtent = int(y * bounds[3] / 100)
yExtent = int(bounds[3])
xEntent = int(bounds[2])
# Coordinates are around the center
xCoord = int(bounds[0] - bounds[2]/2)
yCoord = int(bounds[1] - bounds[3]/2)
boundingBox = [
[xCoord, yCoord],
[xCoord, yCoord + yExtent],
[xCoord + xEntent, yCoord + yExtent],
[xCoord + xEntent, yCoord]
]
# Wiggle it around to make a 3px border
rr, cc = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] for x in boundingBox], shape= shape)
rr2, cc2 = draw.polygon_perimeter([x[1] + 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape)
rr3, cc3 = draw.polygon_perimeter([x[1] - 1 for x in boundingBox], [x[0] for x in boundingBox], shape= shape)
rr4, cc4 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] + 1 for x in boundingBox], shape= shape)
rr5, cc5 = draw.polygon_perimeter([x[1] for x in boundingBox], [x[0] - 1 for x in boundingBox], shape= shape)
boxColor = (int(255 * (1 - (confidence ** 2))), int(255 * (confidence ** 2)), 0)
draw.set_color(image, (rr, cc), boxColor, alpha= 0.8)
draw.set_color(image, (rr2, cc2), boxColor, alpha= 0.8)
draw.set_color(image, (rr3, cc3), boxColor, alpha= 0.8)
draw.set_color(image, (rr4, cc4), boxColor, alpha= 0.8)
draw.set_color(image, (rr5, cc5), boxColor, alpha= 0.8)
if not makeImageOnly:
io.imshow(image)
io.show()
detections = {
"detections": detections,
"image": image,
"caption": "\n".join(imcaption)
}
except Exception as e:
print("Unable to show image: "+str(e))
return detections
if __name__ == "__main__":
print(performDetect())
```
