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updated image paths
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@@ -57,11 +57,11 @@ Here the maximum change from one to the next reading can be limited. If a false
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#### Flow Chart
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[[/images/correct_algo_1.jpg]]
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[[/images/correct_algo_2.jpg]]
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[[/images/correct_algo_3.jpg]]
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@@ -71,4 +71,4 @@ Here the maximum change from one to the next reading can be limited. If a false
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The check digit increase consistency algorithm is functional for the digits only. Due to the fact, that the rotation might be a little bit earlier or later compared to the zero crossing of the digit before, errors during the reading before and after a zero crossing can be wrong. Therefore a simple algorithm can be applied, checking the consistency of zero crossing and changes in the following digit. This is applied to one after the other digit, starting with the lowest priority digits.
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[[/images/correct_algo_zero_crossing.jpg]]
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@@ -46,8 +46,7 @@ Beside the 5V power supply, only for the first flashing a connection to the USB-
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A example for wiring can be found here:
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<img src="https://raw.githubusercontent.com/jomjol/ai-on-the-edge-device/master/images/wiring.png" width="600">
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[[/images/progammer_manual.jpg]]
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It is also possible to use external LEDs for the illumination instead of the internal flash LED. This is described here: [[External-LED]]
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@@ -8,7 +8,7 @@ The neural network configuration is stored in the TensorFlow Lite format as `fil
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In order to incorporate new digits a training set of images is required. The training images needs to be collected in the final setup with the help of the `Digits` or `Analog` log settings (not to be confused with the `Data` or `Debug` log). Enable the logging of the images on the configuration page or in the config file `/config/config.ini`:
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[[/images/enable_log_image.jpg]]
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Now wait, until you have an image of each digit of every type on the SD card. Ideally remove the SD card from the camera and search for two to three images of each digit (**not more! :-)**). The format can be jpg.
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@@ -44,8 +44,8 @@ _______________________________
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| | Classification<br />11 classes<br />0, 1, ... 9 + "N" | Classification<br />100 classes<br />0.0, 0.1, ... 9.9 | Continuous<br />Interval<br />[0, 10[ |
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| ---------------------------------------------------- | ----------------------------------------------------- | ------------------------------------------------------ | ------------------------------------- |
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| **Digits** <br />[[/images/0_arbitrary.jpg]] | **dig-class11**_XXX.tflite | **dig-class100**_XXX.tflite | **dig-cont**_XXX.tflite |
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| **Analog Pointers** <br />[[/images/ana-examp.jpg]] | | **ana-class100**_XXX.tflite | **ana-cont**_XXX.tflite |
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| **Digits** <br /> | **dig-class11**_XXX.tflite | **dig-class100**_XXX.tflite | **dig-cont**_XXX.tflite |
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| **Analog Pointers** <br /> | | **ana-class100**_XXX.tflite | **ana-cont**_XXX.tflite |
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XXX contains the versioning and a parameter for different sizes with the following naming:
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@@ -81,8 +81,8 @@ There are two types of network structure, currently both are supported. The "cla
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| ----------------------------------- | ----------------------------------- | ----------------------------------- | ----------------------------------- |
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| [[/images/ana-cont/examp-ana1.jpg]] | [[/images/ana-cont/examp-ana2.jpg]] | [[/images/ana-cont/examp-ana3.jpg]] | [[/images/ana-cont/examp-ana4.jpg]] |
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| [[/images/ana-cont/examp-ana5.jpg]] | [[/images/ana-cont/examp-ana6.jpg]] | [[/images/ana-cont/examp-ana7.jpg]] | |
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##### Training data needs
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@@ -117,8 +117,8 @@ The digit type is a classical classification network, with 11 classes representi
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| | | | | | | |
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| -------------------------- | -------------------------- | -------------------------- | -------------------------- | -------------------------- | -------------------------- | -------------------------- |
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| [[/images/dig-class11/examp-dig1.jpg]] | [[/images/dig-class11/examp-dig2.jpg]] | [[/images/dig-class11/examp-dig3.jpg]] | [[/images/dig-class11/examp-dig4.jpg]] | [[/images/dig-class11/examp-dig13.jpg]] | [[/images/dig-class11/examp-dig12.jpg]] | [[/images/dig-class11/examp-dig9.jpg]] |
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| [[/images/dig-class11/examp-dig5.jpg]] | [[/images/dig-class11/examp-dig6.jpg]] | [[/images/dig-class11/examp-dig7.jpg]] | [[/images/dig-class11/examp-dig8.jpg]] | [[/images/dig-class11/examp-dig11.jpg]] | [[/images/dig-class11/examp-dig10.jpg]] | |
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@@ -152,7 +152,7 @@ This type of network tries to overcome the problem, that there are intermediate
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| | | | |
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| ---------------------------------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ---- |
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| [[images/dig-cont/dig-cont_1.jpg]] | [[images/dig-cont/dig-cont_2a.jpg]] [[images/dig-cont/dig-cont_2b.jpg]] | [[images/dig-cont/dig-cont_3a.jpg]] [[images/dig-cont/dig-cont_3b.jpg]] [[images/dig-cont/dig-cont_3c.jpg]] | |
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| [[images/dig-cont/dig-cont_1.jpg) | [[images/dig-cont/dig-cont_2a.jpg) [[images/dig-cont/dig-cont_2b.jpg) | [[images/dig-cont/dig-cont_3a.jpg) [[images/dig-cont/dig-cont_3b.jpg) [[images/dig-cont/dig-cont_3c.jpg) | |
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@@ -26,7 +26,7 @@ Ensure an **exact horizontal alignment** of the number via the alignment / refer
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| :heavy_check_mark: Okay | :x: Not Okay |
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| ------------------------------ | ---------------------------------- |
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| [[/images/alignment_okay.jpg]] | [[/images/alignment_not_okay.jpg]] |
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### 3. Correct Size for ROI
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Choose the right size of the ROI:
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@@ -42,9 +42,9 @@ For this model, there should be a border of 20% of the image size around the num
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| | Example 1 | Example 2 |
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| ------------ | --------------------------------- | --------------------------------- |
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| :heavy_check_mark: **Okay** | [[/images/bw_okay.jpg]] | [[/images/wb_okay.jpg]] |
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| :x: **Not** Okay | [[/images/bw_not_okay_small.jpg]] | [[/images/wb_not_okay_small.jpg]] |
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| :x: **Not** Okay | [[/images/bw_not_okay_big.jpg]] | [[/images/wb_not_okay_big.jpg]] |
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| :heavy_check_mark: **Okay** |  |  |
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| :x: **Not** Okay |  |  |
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| :x: **Not** Okay |  |  |
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