No objects found with FOMO on ESP-EYE with ESP-IDF

Help
#1

Hey there,
I’m running a object detection model (FOMO) on an ESP-EYE. I used ESP-IDF instead of Arduino to flash the C++ Code, and with the changes to the code mentioned here:

it worked. However, I’m always getting the message:

run_classifier returned: 0
Predictions (DSP: 11 ms., Classification: 4893 ms., Anomaly: 0 ms.): 
Predictions (DSP: 11 ms., Classification: 4893 ms., Anomaly: 0 ms.): 
    No objects found
]

in the monitor. When running the model on my phone or via the CLI when flashing the packaged firmware for ESP-EYE, it is detecting objects.

I need it to work this way, because I want to send the results of the detection to another controller via bluetooth. So every help would be highly appreciated. Thanks in advance!

#2

btw I’ve been following this: https://github.com/edgeimpulse/example-standalone-inferencing-espressif-esp32

#3

Hi,

This means that the camera detects no object, and the visual field is empty.
You can print the number of objects found as well by modifying the above code that you have accordingly.

Regards,
Omar

#4

Hi @OmarShrit ,
thanks for the reply. I get that there is no object found, what I don’t get is, why it says so. There is an object in the field of view and when pointing a smartphone camera of a smartphone that the model runs on in the same direction, the smartphone is detecting the object. Also, when I’m flashing the pre-built firmware for the ESP-EYE to the ESP-EYE, the object is detected. But as soon as I’m building the firmware myself with ESP-IDF the camera is either not detected or no objects are detected. So what am I missing? I’m following the tutorial…

#5

Hi,

Can you try to use the edge-impulse-daemon with the firmware that you have built and then sample dataset from the studio?
Also you can use Live classification from the studio to see what is happening after capturing the image,

Regards,
Omar

#6

When trying to use edge-impulse-daemon, I’m always getting the following problem:

[SER] Connecting to /dev/tty.usbserial-1420
[SER] Serial is connected, trying to read config...
[SER] Failed to get info off device Timeout when waiting for >  (timeout: 5000) onConnected
#7

Hi,

Do you get the same issue when you use the edge-impulse-run-impulse?

#8

Yes, kind of:

❯ edge-impulse-run-impulse
Edge Impulse impulse runner v1.15.1
? Which device do you want to connect to? /dev/tty.usbserial-1420 (Silicon Labs)
[SER] Connecting to /dev/tty.usbserial-1420
[SER] Serial is connected, trying to read config...
[SER] Failed to get info off device:undefined. Is this device running a binary built through Edge Impulse? Reconnecting in 5 seconds...
#9

Hi,

How did you get the above results about the prediction?

Because if the runner is not working, it is probably that the device is not flashed correctly.

#10

Inside VSCode I used the ESP-IDF extension and built the project, flashed the device and then run the monitor… There it’s giving me the mentioned output

#11

Maybe some more insight is helpful:

esp/esp-idf/tools/idf.py' '-p' '/dev/cu.usbserial-1420'"...
--- idf_monitor on /dev/cu.usbserial-1420 115200 ---
--- Quit: Ctrl+] | Menu: Ctrl+T | Help: Ctrl+T followed by Ctrl+H ---
ets Jun  8 2016 00:22:57

rst:0x1 (POWERON_RESET),boot:0x13 (SPI_FAST_FLASH_BOOT)
configsip: 0, SPIWP:0xee
clk_drv:0x00,q_drv:0x00,d_drv:0x00,cs0_drv:0x00,hd_drv:0x00,wp_drv:0x00
mode:DIO, clock div:2
load:0x3fff0030,len:6604
ho 0 tail 12 room 4
load:0x40078000,len:14780
load:0x40080400,len:3792
0x40080400: _init at ??:?

entry 0x40080694
I (28) boot: ESP-IDF v4.4 2nd stage bootloader
I (29) boot: compile time 10:35:08
I (29) boot: chip revision: 1
I (31) boot_comm: chip revision: 1, min. bootloader chip revision: 0
I (38) boot.esp32: SPI Speed      : 40MHz
I (43) boot.esp32: SPI Mode       : DIO
I (47) boot.esp32: SPI Flash Size : 4MB
I (52) boot: Enabling RNG early entropy source...
I (57) boot: Partition Table:
I (61) boot: ## Label            Usage          Type ST Offset   Length
I (68) boot:  0 nvs              WiFi data        01 02 00009000 00006000
I (76) boot:  1 phy_init         RF data          01 01 0000f000 00001000
I (83) boot:  2 factory          factory app      00 00 00010000 00100000
I (91) boot:  3 storage          Unknown data     01 82 00110000 000f0000
I (98) boot: End of partition table
I (102) boot_comm: chip revision: 1, min. application chip revision: 0
I (109) esp_image: segment 0: paddr=00010020 vaddr=3f400020 size=2282ch (141356) map
I (169) esp_image: segment 1: paddr=00032854 vaddr=3ffb0000 size=02bbch ( 11196) load
I (174) esp_image: segment 2: paddr=00035418 vaddr=40080000 size=0ac00h ( 44032) load
I (193) esp_image: segment 3: paddr=00040020 vaddr=400d0020 size=27c64h (162916) map
I (252) esp_image: segment 4: paddr=00067c8c vaddr=4008ac00 size=04358h ( 17240) load
I (260) esp_image: segment 5: paddr=0006bfec vaddr=50000000 size=00010h (    16) load
I (267) boot: Loaded app from partition at offset 0x10000
I (267) boot: Disabling RNG early entropy source...
Edge Impulse standalone inferencing (Espressif ESP32)
run_classifier returned: 0
Predictions (DSP: 11 ms., Classification: 4893 ms., Anomaly: 0 ms.): 
Predictions (DSP: 11 ms., Classification: 4893 ms., Anomaly: 0 ms.): 
    No objects found
]
#12

@OmarShrit there is an update:
I am able to build this firmware: https://github.com/edgeimpulse/firmware-espressif-esp32 and flash it onto the ESP-EYE so that i can run the edge-impulse-run-impulse command that runs the audio impulse hello-world example. However, if I replace the model and everything necessary with the files that I get by downloading the C++ code from my edge impulse project, the above mentioned problems occur and I cannot use the edge-impulse-run-impulse command. I was unable to check if it’s also working with the camera example as i did not understand how to switch between them just yet.

I think something is missing in the standalone inferencing example, just adding or exchanging the C++ files downloaded from the project doesn’t seem to do the trick here …

#13

Hi Nordcode,

Sorry for my misunderstanding. I thought you had modified the firmware, and then you were building it. Of course, the standalone example is not intended to work with our CLI.

Can I ask what the content of the feature vector that you are trying to classify is?
Would it be possible to print it?

#14

Hi @OmarShrit,

I’m not sure if I get your question right.
I’m trying to count bees in an image with FOMO.
My feature vector looks like this (raw features copied from “live classification”):

static const float features[] = {
    // copy raw features here (for example from the 'Live classification' page)
    0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x4c4c4b, 0x464341, 0x484240, 0x4e4a45, 0x515049, 0x53534f, 0x5f5e5d, 0x5d5a5c, 0x575450, 0x585454, 0x645f5e, 0x564f52, 0x807964, 0xa8a75e, 0xada86a, 0x979568, 0x4b553e, 0x496336, 0x56673f, 0x5a6b42, 0x516636, 0x516533, 0x506533, 0x495f31, 0x4e6137, 0x53683d, 0x52673b, 0x516638, 0x596e41, 0x4b6034, 0x44582d, 0x516439, 0x4b6233, 0x506939, 0x4b6335, 0x4a6132, 0x4e6534, 0x4e6433, 0x4f6734, 0x556c3c, 0x586d39, 0x5d7137, 0x4f602d, 0x5a702e, 0x5d7431, 0x4d6227, 0x475b29, 0x44572b, 0x50632f, 0x5f7440, 0x4c612e, 0x4a602b, 0x60783e, 0x7a9449, 0x78903d, 0x647630, 0x5f6f3a, 0x59663b, 0x4c5930, 0x697949, 0x768752, 0x809058, 0x77854d, 0x7f8f4c, 0x71853a, 0x536928, 0x586d3b, 0x627a47, 0x5d7242, 0x82935e, 0x6e8143, 0x607436, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x3c3938, 0x3e3938, 0x47403d, 0x47413d, 0x4e4b45, 0x504b48, 0x5d5958, 0x5c5757, 0x4a4442, 0x575152, 0x696463, 0x484245, 0x6e6757, 0xbcb67b, 0xa19944, 0xccc875, 0x62673f, 0x475c2e, 0x4d5b38, 0x54643e, 0x4a5d32, 0x485b2e, 0x45592c, 0x506439, 0x566940, 0x495d33, 0x4e6337, 0x586d40, 0x485d2f, 0x465b2f, 0x3f5328, 0x4b5e33, 0x4d6433, 0x526a39, 0x50683b, 0x475e2e, 0x4c6231, 0x526837, 0x526835, 0x435828, 0x4d612d, 0x65773d, 0x51602b, 0x5d7331, 0x5d7330, 0x556a2f, 0x506531, 0x4f6331, 0x4f6330, 0x4a5e2e, 0x526637, 0x4c602f, 0x4f6431, 0x566b29, 0x829842, 0x839745, 0x677c36, 0x70854b, 0x546433, 0x55643a, 0x5d693f, 0x72784c, 0x9ea768, 0x8d9a4d, 0x85954e, 0x758849, 0x788e50, 0x708748, 0x6a7e41, 0x748648, 0x708545, 0x607635, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x433d3b, 0x484341, 0x55504d, 0x524d4a, 0x4e4744, 0x534a47, 0x504843, 0x504843, 0x544e4b, 0x54504f, 0x534f4e, 0x3e3939, 0x433930, 0xaa9e6f, 0x9c9334, 0xd6d270, 0x767b4a, 0x506437, 0x4d6036, 0x4d5f36, 0x495c34, 0x465930, 0x4b5e36, 0x495c33, 0x4d6035, 0x4b5d34, 0x516539, 0x4e6336, 0x465b2e, 0x485d30, 0x405328, 0x475b30, 0x556b3c, 0x4a6231, 0x4d6534, 0x445c2a, 0x455c29, 0x4f6531, 0x4d622e, 0x4d6130, 0x647544, 0x96a56f, 0x516029, 0x647739, 0x596c2e, 0x4a5d23, 0x657a42, 0x61773e, 0x526733, 0x4a5e2c, 0x4f6336, 0x4b6032, 0x526338, 0x4f5f2a, 0x677b29, 0x849941, 0x778e40, 0x7c954d, 0x718841, 0x677741, 0x666f3e, 0x737642, 0x89914a, 0x859344, 0x647131, 0x7c8d52, 0x697f39, 0x6e863c, 0x7e9248, 0x6b7f37, 0x687c3d, 0x6d8642, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x4f4b47, 0x5c5b56, 0x5d5d58, 0x5b5855, 0x4f4845, 0x584d45, 0x594b42, 0x51453b, 0x544d48, 0x4e4a46, 0x4a4443, 0x4f4a45, 0x392f29, 0xac9c73, 0x9f9636, 0xddd978, 0x939666, 0x4c5d38, 0x54683d, 0x4c5b32, 0x4c5e37, 0x4d613b, 0x4d6039, 0x50633a, 0x4c5f36, 0x405329, 0x485c30, 0x4e6335, 0x465b2e, 0x44582e, 0x43562d, 0x43562d, 0x4b5f33, 0x516938, 0x4c6433, 0x4b6230, 0x445a27, 0x4d6331, 0x516931, 0x4c622d, 0x576a39, 0x62743d, 0x50602b, 0x57672f, 0x52632a, 0x4e612b, 0x596e34, 0x6a8242, 0x587134, 0x556c35, 0x4c5f2f, 0x505f33, 0x5b6a3f, 0x5c6d3c, 0x798b48, 0x768842, 0x637435, 0x6c7f3e, 0x80944d, 0x7e8f51, 0x94a26a, 0x9ca56b, 0x808c4d, 0x717f41, 0x65723f, 0x637242, 0x596b2d, 0x617a2f, 0x778f3b, 0x63762d, 0x607236, 0x697f3b, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x484643, 0x53534e, 0x5a5b56, 0x5b5a58, 0x4e4846, 0x978b82, 0xefe7d8, 0xa49789, 0x4c423c, 0x5a5350, 0x544e50, 0x45413d, 0x584f45, 0xbaab7c, 0xd8cd6c, 0xc4bd58, 0xa8a375, 0x3d4425, 0x485333, 0x4c5930, 0x465530, 0x4b5d38, 0x4d6037, 0x55693c, 0x5a6d43, 0x4c6036, 0x465a2e, 0x4b6033, 0x415529, 0x42552d, 0x455832, 0x455930, 0x405429, 0x4a6133, 0x4c6534, 0x4f6634, 0x445a26, 0x415723, 0x566e35, 0x516832, 0x4e6431, 0x576c36, 0x606f3b, 0x65733f, 0x4f5d2a, 0x556633, 0x52672f, 0x577031, 0x5c7535, 0x5c7337, 0x576b36, 0x516131, 0x506230, 0x617540, 0x62723c, 0x6c7946, 0x6c794a, 0x606e3d, 0x687644, 0x70804d, 0x8a9b66, 0x7f8e5d, 0x5d6d3d, 0x6e804f, 0x768458, 0x58683e, 0x5a693a, 0x5f7632, 0x6b8631, 0x6d8138, 0x607037, 0x657933, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x4f4e4c, 0x545251, 0x4b4948, 0x474341, 0x423a39, 0x95847e, 0xfffcea, 0xe0d8c1, 0x6b5e53, 0x6c615d, 0x565151, 0x3a3539, 0x726659, 0xccbc86, 0xe4d980, 0xa09635, 0xb9ab7e, 0x676740, 0x585c32, 0x535d31, 0x51643a, 0x52623d, 0x4f5f36, 0x4c6230, 0x516638, 0x506438, 0x55683f, 0x4d6238, 0x495c34, 0x485834, 0x495c34, 0x3d5027, 0x3e532a, 0x51653d, 0x536940, 0x4c6231, 0x465d28, 0x3d521e, 0x425623, 0x506432, 0x4c612e, 0x546631, 0x68763e, 0x727b44, 0x5c6932, 0x566933, 0x4a5f2d, 0x4b5f30, 0x5d723c, 0x60753a, 0x516630, 0x556b34, 0x657b3d, 0x667740, 0x677446, 0x828e5f, 0x7b8a5a, 0x768959, 0x5b6e45, 0x6e7c58, 0x80925b, 0x607442, 0x61754b, 0x607443, 0x6c7c4c, 0x69784e, 0x556436, 0x627838, 0x77913e, 0x738a3c, 0x718347, 0x718440, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x484745, 0x494846, 0x474644, 0x484342, 0x776f70, 0x998787, 0xe4d0c6, 0xfce7d7, 0xad9c89, 0x5e544a, 0x74706a, 0x655f59, 0x9a8b7e, 0xb5a253, 0xcac33b, 0xbeb63d, 0xdbcb8d, 0xe5d8b7, 0xddd2ad, 0x9b9b7a, 0x526137, 0x586d3f, 0x5b6d40, 0x566b3f, 0x4c6030, 0x45592b, 0x4d6136, 0x51643c, 0x546840, 0x465931, 0x42552d, 0x3e512b, 0x475b34, 0x4a5c38, 0x3b4c28, 0x405229, 0x4d6031, 0x546a37, 0x425723, 0x4e6230, 0x4f6331, 0x536632, 0x62723d, 0x6b7941, 0x65753d, 0x60733d, 0x576c38, 0x506431, 0x586d36, 0x556b33, 0x4f6331, 0x5b6f3d, 0x63773f, 0x556a34, 0x6c7e4e, 0x92a473, 0x6b7f4b, 0x647849, 0x778762, 0x56653f, 0x586935, 0x596c3b, 0x5b6f42, 0x566b39, 0x607242, 0x738354, 0x62743c, 0x677d36, 0x809649, 0x6b7e3b, 0x626f3b, 0x6a763e, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x0, 0x494846, 0x555452, 0x4c4b49, 0x423d3c, 0x676163, 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...```
#15 
...

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it’s in main/main.cpp of the standalone inferencing example.

Sorry, it’s been too long i had to skip a part in the middle…

#16

Hi,

Indeed, It seems strange that you are getting these results.
Also how many bees are in the image you are trying to detect?

@AIWintermuteAI do you have an idea what is happening here?

#17

In the particular image that I’m testing on, it’s only one bee. But I’ve been testing multiple images. With my smartphone and the pre-built firmware all this is working…

#18

Okay, so the issue is that static buffer example does not detect an object?
Can you post a full code somewhere? Or share a project folder

#19

@AIWintermuteAI
you are right, thats the issue. I just sent you a PM with a link to the project code.

#20

@AIWintermuteAI thanks for your help so far!
okay, so obviously my problem was, that I took the raw features of a sample with no object in it. I’m sorry. I think i totally misunderstood what the feature vector of the static buffer example is meant to do. So for a general model, that can tell wether in any given image there is a bee or a number of bees, what kind of features do i need to use? if any? i’m not sure wether the static buffer example is good for me, as i’m just trying to get the result of the object detection to pass it to another microcontroller. I’m sorry for all the confusion.