Hi iI have a small GUI that contains 1 'Push Button' and 3 'Edit Texts' and a few static text labels to display the results.
What I want to do is to be able to calculate from a series of numbers their: sum, average, min, max, Standard Deviation and Skewness
The user will enter the following data [using Edit Text boxes]:
Start Number of the sequence
End Number of the sequence
Increment step
And by using a Pushbutton all the above results will be returned in separate static texts.
I am very new to MATLAB can anyone push me into the direction i need to go inorder to achieve this.
My user interface if any help:
A simple solution should be :
function pushbutton1_Callback(hObject, eventdata, handles)
%[
startValue = str2num(get(handles.edit1,'string')) ;
stopValue = str2num(get(handles.edit2,'string')) ;
step = str2num(get(handles.edit3,'string')) ;
series = startValue:step:stopValue ;
average = mean(series) ;
minValue = min(series) ;
...
...
set(handles.text1,'string',average);
set(handles.text2,'string',minValue);
...
%]
Hope it will be helpful !
You might find these 41 complete GUI examples useful...
It'll answer you these questions:
1.How do I manipulate the strings in a uicontrol? GUI_1, 2, 4, 5, 13, 14, 15, 20, 21, 22, 37
2.How do make a uicontrol invisible/visible? GUI_3, 35 (See also GUI_10 for images)
3.How do I make a multi-line edit box? GUI_4
4.How can I initialize an editbox so that the cursor is blinking at startup? GUI_4, 24, 37
5.How can I let the user of my GUI know his actions are futile (or producing no results)? GUI_5
6.How can I tell which uicontrol is selected e.g., radiobuttons? GUI_6, 8
7.How do I tell how many times a uicontrol has been activated? GUI_7, 19, 28, 32, 33
8.How do I tell which button in a buttongroup is selected? GUI_8
9.How do I let the user know a process is running in the background? GUI_9
10.How can I set an image visible/invisible? GUI_10
11.How can I use a GUI to exit a FOR loop? GUI_11
12.How can I control the mouse pointer with a GUI? GUI_12
13.How do I access the value (current position) of a slider? GUI_13, 16
14.How do I use different colored strings in a listbox? GUI_14
15.What is the difference between 'listboxtop' and 'value' in a listbox? GUI_14
16.How do I make text that can be copied but not changed? GUI_15
17.How can I allow the user of my GUI to set the range of a slider? GUI_16
18.How can I display a digital clock in my GUI? GUI_17
19.How can I use a timer in a GUI? GUI_17
20.How do I use the buttondownfcn on an axes object? GUI_18, 28
21.How do I make a callback talk to another callback? GUI_19
22.How can I get the string from a popup or listbox? GUI_14, 20, 21, 22, 31, 32, 33
23.How can I set the string in a popup or listbox? GUI_21, 22
24.How can I add to the string in a popup or listbox? GUI_22
25.How do I tell which figure/axes was current before my callback executed? GUI_23
26.How do I get data from another GUI? GUI_24
27.How do I make a GUI to open image files only? GUI_25
28.How can I make popup choices mutually exclusive? GUI_26
29.How can I show the current pointer location in axes coordinates? GUI_27
30.How can I use uicontextmenus? GUI_28, 33, 39
31.How do I make my GUI control an axes in another figure? GUI_29, 30
32.What are callback strings? GUI_30
33.How can I make it so that when one of the figures closes, they all close? GUI_24, 29,
30, 41
34.How do I make several uicontrols interact in a more complicated GUI? GUI_31, 32, 33, 41
35.How do I get data from a GUI to the base workspace? GUI_25, 32, 33, 36
36.How can I use a GUI to take a screenshot of my desktop? GUI_34
37.How do I make toggle buttons act like tabbed-panels? GUI_35
38.How do I make a custom dialog box which returns a string to the base workspace? GUI_36
39.How can I make a password editbox that has the * symbols? GUI_37
40.How can I use nested function as callbacks? GUI_11, 17, 34, 36, 37, 39, 40, 41.
41.How can I use uiwait in a GUI? GUI_11, 34, 36, 37
42.How do I use JAVA in my GUI? GUI_38
43.How do I force the figure to maintain focus between uicontrol activations? GUI_38
44.How do I save an axes as an image? GUI_39
45.How can I make a simple drawing program? GUI_39
46.How can I set a button's background to match an image? GUI_40
47.How can I save the state of a system of GUIs to use later? GUI_41
Related
I am a beginner to deep learning and I am working with Keras built on top of Tensorflow. I am trying to using RGB images (540 x 360) resolution to predict bounding boxes.
My labels are binary (black/white) 2 dimensional np array of dimensions (540, 360) where all pixels are 0 except for the box edges which are a 1.
Like this:
[[0 0 0 0 0 0 ... 0]
[0 1 1 1 1 0 ... 0]
[0 1 0 0 1 0 ... 0]
[0 1 0 0 1 0 ... 0]
[0 1 1 1 1 0 ... 0]
[0 0 0 0 0 0 ... 0]]
There can be more than one bounding box in every picture. A typical image could look like this:
So, my input has the dimension (None, 540, 360, 3), output has dimensions (None, 540, 360) but if I add an internal array I can change the shape to (None, 540, 360, 1)
How would I define a CNN model such that my model could fit this criteria? How can I design a CNN with these inputs and outputs?
You have do differentiate between object detection and object segmentation. While both can be used for similar problems, the underlying CNN architectures look very different.
Object detection models use a CNN classification/regression architecure, where the output refers to the coordinates of the bounding boxes. It's common practice to use 4 values belonging to vertical center, horizontal center, width and height of each bounding box. Search for Faster R-CNN, SSD or YOLO to find popular object detection models for keras. In your case you would need to define a function that converts the current labels to the 4 coordinates I mentioned.
Object segmentation models commonly use an architecture referred to as encoder-decoder networks, where the original image is scaled down and compressed on the first half and then brought back to it's original resolution to predict a full image. Search for SegNet, U-Net or Tiramisu to find popular object segmentation models for keras. My own implementation of U-Net can be found here. In your case you would need to define a custom function, that fills all the 0s inside your bounding boxes with 1s. Understand that this solution will not predict bounding boxes as such, but segmentation maps showing regions of interest.
What is right for you, depends on what precisely you want to achieve. For getting actual bounding boxes you want to perform an object detection. However, if you're interested in highlighting regions of interest that go beyond rectangle windows a segmentation may be a better fit. In theory, you can use your rectangle labels for a segmentation, where the network will learn to create better masks than the inaccurate segmentation of the ground truth, provided you have enough data.
This is a simple example of how to write intermediate layers to achieve the output. You can use this as a starter code.
def model_360x540(input_shape=(360, 540, 3),num_classes=1):
inputs = Input(shape=input_shape)
# 360x540x3
downblock0 = Conv2D(32, (3, 3), padding='same')(inputs)
# 360x540x32
downblock0 = BatchNormalization()(block0)
downblock0 = Activation('relu')(block0)
downblock0_pool = MaxPooling2D((2, 2), strides=(2, 2))(block0)
# 180x270x32
centerblock0 = Conv2D(1024, (3, 3), padding='same')(downblock0_pool)
#180x270x1024
centerblock0 = BatchNormalization()(center)
centerblock0 = Activation('relu')(center)
upblock0 = UpSampling2D((2, 2))(centerblock0)
# 180x270x32
upblock0 = concatenate([downblock0 , upblock0], axis=3)
upblock0 = Activation('relu')(upblock0)
upblock0 = Conv2D(32, (3, 3), padding='same')(upblock0)
# 360x540x32
upblock0 = BatchNormalization()(upblock0)
upblock0 = Activation('relu')(upblock0)
classify = Conv2D(num_classes, (1, 1), activation='sigmoid')(upblock0)
#360x540x1
model = Model(inputs=inputs, outputs=classify)
model.compile(optimizer=RMSprop(lr=0.001), loss=bce_dice_loss, metrics=[dice_coeff])
return model
The downblock represents the block of layers which perform downsampling(MaxPooling2D).
The centerblock has no sampling layer.
The upblock represents the block of layers which perform up sampling(UpSampling2D).
So here you can see how (360,540,3) is being transformed to (360,540,1)
Basically, you can add such blocks of layers to create your model.
Also check out Holistically-Nested Edge Detection which will help you better with the edge detection task.
Hope this helps!
I have not worked with keras but I will provide a solution approach in more generalized way which can be used on any framework.
Here is full procedure.
Data preparation: I know your labels are edges of boxes which will also work but i will recommend that instead of edges you prepare dataset marking complete box like given in sample (I have marked for two boxes). Now your dataset have three classes (Box,Edges of box and background). Create two lists, Image and label.
Get a pre-trained model (RESNET-51 recommended) solver and train prototxt from here, Remove fc1000 layer and add de-convolution/up-sampling layers to match your input size. use paddding in first layer to make it square and crop in deconvolution layer to match input output dimensions.
Transfer weights from previously trained network (Original) and train your network.
Test your dataset and create bounding boxes using detected blobs.
I am learning machine learning using TensorFlow. I have been through a couple of tutorials but I still have a hard time trying to find what are the good ways of training a model. Recently I implemented a CNN model I found in the litterature. The model must take a crop of a certain size centered on a given pixel and predict the label of this pixel. It does that for each pixel of the image. I used:
classifier = tf.learn.Estimator(model_fn=cnn_model_fn, model_dir="./cnn")
with cnn_model_fn beeing a function I implemented.
For each training image, we take 3000 crops randomly, so I can't load all theses images and their crops to memory. The way I found is by loading one image at a time, extract the 3000 crops and then call classifier.fit() to train on the 3000 crops. Then loop for each image in my dataset.
for i in range(len(filenames)):
...
image = misc.imread(filenames[i])
labels = misc.imread(groundTruth[i]) #labels for each pixels
input_classifier = preprocess(image,...) #crops 3000 images in image and do other things
input_labels = preprocess_labels(labels, ...) #take the corresponding 3000 labels
classifier.fit(x = input_classifier,
y = input_labels,
batch_size = 30
steps = 100)
It worked fine for 100 images, but if I try on the whole dataset (2000 images), it always stops and give an error of ResourceExhausted.
...
[everything goes well]
...
iteration :227/2000
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus
id: 0000:01:00.0)
INFO:tensorflow:Create CheckpointSaverHook.
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: Tesla K40c, pci bus
id: 0000:01:00.0)
Traceback (most recent call last):
File "train-cnn.py", line 78, in <module>
classifier.fit(x= input_classifier, y=input_labels,batch_size=30, steps=100)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/util/deprecation.py", line 280, in new_func
...
...
...
tensorflow.python.framework.errors_impl.ResourceExhaustedError: cnn/graph.pbtxt.tmp32bcc6311c164c29b91177d17d05d669
I don't see why it gets OOM... I have suspicions that it is because of the way I call fit() in loop. After each fit(), a ckpt is saved and it must be restored right after to train on the next image. So is it a bad way to train a model?
running estimator.fit in a loop with smaller steps is not a good idea. I would put all input logic into an input_fn. then run estimator.fit only once with more steps.
An example of reading data from different files can be found here: tf.contrib.learn.read_batch_examples
I am developing an windows application in F#. In the application I have to show the TextBox mode in Password Format. What is the code for using the password mode of TextBox in F#?
I have applied the following code:
let txtpwd = new TextBox(Top = 70, Left = 120)
From the above code the textbox is displaying. No problem. I have applied following code for password mode:
txtpwd.PasswordChar
The above code is not working properly.
You should set desired properties upon initialization of your control, for example:
txtpwd.Text <- "" // Set to no text
txtpwd.PasswordChar <-'*' // The password character is an asterisk
txtpwd.MaxLength <- 14 // The control will allow no more than 14 characters
Better yet, set the properties in your call to the constructor. One of the cool things about F# is that you can set properties in the call that you wouldn't normally be able to set in the constructor. Like this:
let txtpwd = new TextBox(Top = 70, Left = 120, Text = "", PasswordChar = '*',MaxLength = 14, Multiline = true)
This is basically equivalent to what Gene posted but, as far as I know, it's a little more idiomatic in F#.
If you check this page under the topic "Assigning Values To Properties At Initialization" (sorry can't post a direct link) although the page is discussing F# code, it holds for other .Net code as well.
I have two arraylist, x[],y[]. Suppose :
x[0]= 1, y[0]=2,
x[1]= 3, y[1]=3,
x[2]= 4, y[2]=6,
x[3]= 4, y[3]=9,
x[4]= 7, y[4]=22,
x[5]= -4, y[5]=5,
..............
in time delay of 10 sec, the graph goes [0] to [1] and then it goes on in same delay.
How i represent the graph? I think 3d graph is must here. But how do i use it in .Net winform?
You will need to use a component to visuialize the data as a graph.
Check out Microsoft Chart Controls, they are a good option.
I want a rather simple (and cheap) solution, just for presentation purposes (and just to show the task duration bars - no connection lines between them). So, I am not interested in buying some advanced custom control like this for example. Have any of you ever used something like this? Are there any code samples available?
I would have pointed to Buck Woolley's dwExtreme site for an example of how to do a gantt in native DataWindow. However, "simple" I don't think is in your future if you want to roll your own. In fact, I'll be pleasantly surprised if someone writes a posting that includes a full description; I think it would take pages. (I'd be happy if someone proved me wrong.) In the meantime, here are some DataWindow basics I think you would need:
You can create an external DataWindow whose data source is not tied to a table
Columns in the data set do not have to be shown on the user interface
Columns in the data set can be used in expressions to evaluate attributes, so you could have a column for each of the following attributes of a rectangle:
x
width
color
I'd expect this to be a lot of work and time, and very likely to be worth the purchase the component (unless your time is valued at next to nothing, which in some IT shops is close to true).
Good luck,
Terry
(source: illudium.com)
You can make a simple Gantt chart with a Stacked Bar Graph (BarStacked (5) in the painter). The trick is to create a dummy series to space the bar out where you want it and make the dummy bar the same color as the graph's background (BackColor). It turns out you also need another dummy series with a small value to sit on the axis. Otherwise when you change the color of the bar that's doing the spacing, the axis line gets cut off. I found that .04 works well for this value.
Create the DataWindow
(This assumes familarity with the DataWindow Wizard. Refer to the PowerBuilder User's Guide for more information on creating graphs in DataWindows)
Click the icon for the new object wizard. Create a Graph DataWindow with an External data source. Create columns task type string(20), ser type string(1), and days type number. Set the Category to the task column and the Values to the days column. Click the Series button and select ser for the series. Don't bother with the title, and select the Stacked Bar graph type. When the painter opens, save the DataWindow. On the General tab in the Painter, change the Legend to None (0). On the Axis tab, select the Category axis, then set the sort to Unsorted (0). Select the Value axis then set the sort to Unsorted (0). Select the Series axis and set the sort to Ascending (1). Save the DataWindow.
Create the Window
Create a window and place a DataWindow control, dw_1. Set the data object to your graph DataWindow. Place the following in the open event (or pfc_postopen if using PFC).
try
dw_1.setRedraw(FALSE)
// LOAD DATA HERE
dw_1.object.gr_1.title = 'Project PBL Pusher'
dw_1.object.gr_1.category.label = 'Phase'
dw_1.object.gr_1.values.label = 'Project-Days'
catch (runtimeerror re)
if isvalid(gnv_app.inv_debug) then gnv_app.inv_debug.of_message(re.text) // could do better
finally
dw_1.setRedraw(TRUE)
end try
You would load the data for your chart where the comment says // LOAD DATA HERE
Script the graphcreate Event
Add an new event to dw_1. Select pbm_dwngraphcreate for the Event ID. I like to name these events by removing the pbm_dwn prefix so I use graphcreate. Add the following code to the event.
string ls_series
long li_color
try
li_color=long(dw_1.object.gr_1.backcolor)
// note first series is a dummy with a small value (0.04 seems to work) to keep the line from being hidden
ls_series = dw_1.seriesName("gr_1", 2)
if 0 = len(ls_series) then return // maybe show error message
// will return -1 when you set color same as the graph's backcolor but it sets the color
dw_1.setSeriesStyle("gr_1", ls_series, BackGround!, li_color) // the box
dw_1.setSeriesStyle("gr_1", ls_series, ForeGround!, li_color) // the inside
catch (runtimeerror re)
if isvalid(gnv_app.inv_debug) then gnv_app.inv_debug.of_message(re.text) // could do better
end try
Data for the Graph
Load the data with the categories in the reverse order of what you want. For each Task, insert 3 rows and set the series to a, b, and c, respectively. For series a in each task, set a small value. I used 0.04. You may have to experiment. For series b in each task, set the number of days before start. For series c, set the number of days. Below is the data in the sample DataWindow.
Task Ser Days
---- --- ----
Test a 0.04
Test b 24
Test c 10
Develop a 0.04
Develop b 10
Develop c 14
Design a 0.04
Design b 0
Design c 10
Sample DataWindow
Below is the source for a sample DataWindow in export format. You should be able to import into any version >= PB 10. Copy the code and paste it into a file with an SRD extension, then import it.
HA$PBExportHeader$d_graph.srd
release 10;
datawindow(units=0 timer_interval=0 color=1073741824 processing=3 HTMLDW=no print.printername="" print.documentname="" print.orientation = 1 print.margin.left = 110 print.margin.right = 110 print.margin.top = 96 print.margin.bottom = 96 print.paper.source = 0 print.paper.size = 0 print.canusedefaultprinter=yes print.prompt=no print.buttons=no print.preview.buttons=no print.cliptext=no print.overrideprintjob=no print.collate=yes hidegrayline=no )
summary(height=0 color="536870912" )
footer(height=0 color="536870912" )
detail(height=0 color="536870912" )
table(column=(type=char(10) updatewhereclause=yes name=task dbname="task" )
column=(type=char(1) updatewhereclause=yes name=ser dbname="ser" )
column=(type=number updatewhereclause=yes name=days dbname="days" )
)
data("Test","a", 0.04,"Test","b", 24,"Test","c", 10,"Develop","a", 0.04,"Develop","b", 10,"Develop","c", 14,"Design","a", 0.04,"Design","b", 0,"Design","c", 10,)
graph(band=background height="1232" width="2798" graphtype="5" perspective="2" rotation="-20" color="0" backcolor="16777215" shadecolor="8355711" range= 0 border="3" overlappercent="0" spacing="100" plotnulldata="0" elevation="20" depth="100"x="0" y="0" height="1232" width="2798" name=gr_1 visible="1" sizetodisplay=1 series="ser" category="task" values="days" title="Title" title.dispattr.backcolor="553648127" title.dispattr.alignment="2" title.dispattr.autosize="1" title.dispattr.font.charset="0" title.dispattr.font.escapement="0" title.dispattr.font.face="Tahoma" title.dispattr.font.family="2" title.dispattr.font.height="0" title.dispattr.font.italic="0" title.dispattr.font.orientation="0" title.dispattr.font.pitch="2" title.dispattr.font.strikethrough="0" title.dispattr.font.underline="0" title.dispattr.font.weight="700" title.dispattr.format="[general]" title.dispattr.textcolor="0" title.dispattr.displayexpression="title" legend="0" legend.dispattr.backcolor="536870912" legend.dispattr.alignment="0" legend.dispattr.autosize="1" legend.dispattr.font.charset="0" legend.dispattr.font.escapement="0" legend.dispattr.font.face="Tahoma" legend.dispattr.font.family="2" legend.dispattr.font.height="0" legend.dispattr.font.italic="0" legend.dispattr.font.orientation="0" legend.dispattr.font.pitch="2" legend.dispattr.font.strikethrough="0" legend.dispattr.font.underline="0" legend.dispattr.font.weight="400" legend.dispattr.format="[general]" legend.dispattr.textcolor="553648127" legend.dispattr.displayexpression="' '"
series.autoscale="1"
series.displayeverynlabels="0" series.droplines="0" series.frame="1" series.label="(None)" series.majordivisions="0" series.majorgridline="0" series.majortic="3" series.maximumvalue="0" series.minimumvalue="0" series.minordivisions="0" series.minorgridline="0" series.minortic="1" series.originline="1" series.primaryline="1" series.roundto="0" series.roundtounit="0" series.scaletype="1" series.scalevalue="1" series.secondaryline="0" series.shadebackedge="0" series.dispattr.backcolor="536870912" series.dispattr.alignment="0" series.dispattr.autosize="1" series.dispattr.font.charset="0" series.dispattr.font.escapement="0" series.dispattr.font.face="Tahoma" series.dispattr.font.family="2" series.dispattr.font.height="0" series.dispattr.font.italic="0" series.dispattr.font.orientation="0" series.dispattr.font.pitch="2" series.dispattr.font.strikethrough="0" series.dispattr.font.underline="0" series.dispattr.font.weight="400" series.dispattr.format="[general]" series.dispattr.textcolor="0" series.dispattr.displayexpression="series" series.labeldispattr.backcolor="553648127" series.labeldispattr.alignment="2" series.labeldispattr.autosize="1" series.labeldispattr.font.charset="0" series.labeldispattr.font.escapement="0" series.labeldispattr.font.face="Tahoma" series.labeldispattr.font.family="2" series.labeldispattr.font.height="0" series.labeldispattr.font.italic="0" series.labeldispattr.font.orientation="0" series.labeldispattr.font.pitch="2" series.labeldispattr.font.strikethrough="0" series.labeldispattr.font.underline="0" series.labeldispattr.font.weight="400" series.labeldispattr.format="[general]" series.labeldispattr.textcolor="0" series.labeldispattr.displayexpression=" seriesaxislabel" series.sort="1"
category.autoscale="1"
category.displayeverynlabels="0" category.droplines="0" category.frame="1" category.label="(None)" category.majordivisions="0" category.majorgridline="0" category.majortic="3" category.maximumvalue="0" category.minimumvalue="0" category.minordivisions="0" category.minorgridline="0" category.minortic="1" category.originline="0" category.primaryline="1" category.roundto="0" category.roundtounit="0" category.scaletype="1" category.scalevalue="1" category.secondaryline="0" category.shadebackedge="1" category.dispattr.backcolor="556870912" category.dispattr.alignment="1" category.dispattr.autosize="1" category.dispattr.font.charset="0" category.dispattr.font.escapement="0" category.dispattr.font.face="Tahoma" category.dispattr.font.family="2" category.dispattr.font.height="0" category.dispattr.font.italic="0" category.dispattr.font.orientation="0" category.dispattr.font.pitch="2" category.dispattr.font.strikethrough="0" category.dispattr.font.underline="0" category.dispattr.font.weight="400" category.dispattr.format="[general]" category.dispattr.textcolor="0" category.dispattr.displayexpression="category" category.labeldispattr.backcolor="556870912" category.labeldispattr.alignment="2" category.labeldispattr.autosize="1" category.labeldispattr.font.charset="0" category.labeldispattr.font.escapement="900" category.labeldispattr.font.face="Tahoma" category.labeldispattr.font.family="2" category.labeldispattr.font.height="0" category.labeldispattr.font.italic="0" category.labeldispattr.font.orientation="900" category.labeldispattr.font.pitch="2" category.labeldispattr.font.strikethrough="0" category.labeldispattr.font.underline="0" category.labeldispattr.font.weight="400" category.labeldispattr.format="[general]" category.labeldispattr.textcolor="0" category.labeldispattr.displayexpression="categoryaxislabel" category.sort="0"
values.autoscale="1"
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xhtmlgen() cssgen(sessionspecific="0" )
xmlgen(inline="0" )
xsltgen()
jsgen()
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export.pdf(method=0 distill.custompostscript="0" xslfop.print="0" )
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