Firmware update broke your Python vision script: fix the API call

A script that ran every shift stops on the first frame after a camera firmware update, with an error it never produced before: extra positional arguments given. Nothing in your code changed; the library under it did. This guide is for anyone who maintains Python vision scripts, on cameras that run Python on the device or on a PC next to the camera. You will read the failure precisely, find the new function signature for the installed build, and rewrite the call so it matches again. Then you prove the fix on one captured frame before the production loop runs. Budget about half an hour at the bench if you know where the release notes live and you kept one golden frame.

Why does a script that ran yesterday fail right after an update?

Because the update replaced the Python library your script imports, and one function it calls now has a different signature. The new build renamed a parameter, added a required one or dropped one you still pass. The interpreter rejects a call that was legal an hour earlier.

On a camera that runs inspection on the device, the interpreter, the camera library and the firmware ship as one image, so an update replaces all three at once. Your file is byte-for-byte the same as yesterday's, but every import now resolves against the new build. A PC-side script fails the same way when the camera SDK package updates underneath it: caller and callee are suddenly different versions. The error lands on the first call of the changed function. That is why the setup lines above it run clean and the loop dies on its first pass. On smart cameras the firmware and the library move together; on a PC setup the SDK package is the part that moved.

What does 'extra positional arguments given' actually mean?

It means the function was handed more positional arguments than its new definition accepts. The interpreter matched arguments to parameters by position, ran out of parameters, and raised a TypeError rather than guess. Desktop Python words the same failure as 'takes N positional arguments but M were given'.

Positional arguments bind by order, so an inserted or removed parameter breaks them. Keyword arguments bind by name, so they survive reordering; when a name disappears they fail with a different message, 'unexpected keyword argument', which names the problem outright. The count in the desktop wording tells you the size of the mismatch. 'takes 4 positional arguments but 5 were given' means exactly one argument too many. The same arity error hits other automation software after its libraries update; a SCADA package's named-query transaction signature error is the identical class of failure. It is fixed the same way, by matching the call to the new signature.

Which line is broken, your code or the library?

The traceback names the line of your code that could not finish, and that call site is where your code and the library's new signature disagree. The line number is where you start, not proof that your code is wrong.

Read the traceback from the bottom up. The last line states the error and the message; the frames above it walk from your file into the library's internals. Your line is the meeting point of two versions. In a typical detection script the failing line is the net.detect call inside the loop, while the sensor setup above it ran without complaint, because the functions those lines call did not change. When the project compiles through an SDK wrapper, the same mismatch surfaces even earlier, at build time. Fixing camera SDK build errors after a library upgrade walks that variant, and diagnosing a ROS camera SDK API mismatch at build time shows the compiler catching the identical problem.

How do you find the new signature before touching any code?

Read the release notes for the exact build you just installed, then confirm on the camera itself. Print the changed function's doc string in the REPL where the build ships doc strings, and treat what prints as the final word over any older PDF.

Work the sources in order. First the release notes: search them for the module name your script imports, tf in a typical detection script, and read every line the search returns, because a reorganized module changes more than one function. Second the installed module: print(tf.load.doc) in the REPL shows what the build actually ships. Where a build ships no doc strings, the vendor's API reference for that exact version is the source. Third the version-matched reference: match the version number on the page to the string the camera reports, never to the newest release. Write down the call that worked and the version that ran it; the diff between those two states is the whole diagnosis.

Libraries change signatures on purpose. An interface a vendor has not declared stable can change between versions, and a major update sometimes does it with little notice. The release notes for the exact build are the contract for what changed, so read them before the update rather than after the failure, and pin your script to the versions they describe.

How do you rewrite the call so it matches again?

Pass every argument by name as a keyword argument, give each value the type the new signature shows, and delete any argument it no longer lists. Keyword calls survive parameter reordering and newly added optional parameters that positional calls do not.

# Ran on the old build
min_confidence = 0.5                                   # float, production working point
thresholds = [(math.ceil(min_confidence * 255), 255)]  # list of (int, int) pairs
for i, detection_list in enumerate(net.detect(img, thresholds)):
    pass  # loop body unchanged

# After reading the new signature, call by name
# parameter names below are illustrative; your build's reference shows the real ones
detections = net.detect(img=img, thresholds=thresholds)

If the name is gone, the interpreter answers 'unexpected keyword argument' and names it, which is a two-minute fix. The dangerous case is the type. A thresholds entry of (128, 255) means a score of 128 out of 255; if the new build reads 0.0 to 1.0 floats, the same numbers demand a score above the possible maximum and nothing is ever detected, with no error at all. Check the type column of the reference, not just the names.

The load line has its own arithmetic

The model-loading line in a typical detection script decides where the network is allocated, and its inputs move when the runtime changes:

net = tf.load('trained.tflite', load_to_fb=uos.stat('trained.tflite')[6] > (gc.mem_free() - (64*1024)))

Worked example with illustrative values: the model file is 40,000 bytes, so uos.stat('trained.tflite')[6] returns 40000, and gc.mem_free() reports 98,304 bytes of free heap. The margin is 64 × 1024 = 65,536 bytes, so the comparison is 40,000 > (98,304 - 65,536), that is 40,000 > 32,768, which is True. The model loads into frame-buffer memory and the script keeps the 64 KiB margin for everything else. After an update, heap_free moves because the new runtime reserves memory differently, so this line can flip its decision even though you never edited it. Print both numbers once at startup so you know which way the new build decided.

How do you prove the fix on one frame before restoring the loop?

Capture one frame before the while loop, run the fixed call on that frame alone, and compare the detection count and each score against a saved golden output. A threshold of 128 is your min_confidence of 0.5 because ceil(0.5 × 255) = 128.

min_confidence = 0.5                                    # float, production working point
thresholds = [(math.ceil(min_confidence * 255), 255)]   # list of (int, int) pairs
img = sensor.snapshot()                                 # one frame, captured before the loop
n = 0                                                   # int, detections on this frame
for i, detection_list in enumerate(net.detect(img=img, thresholds=thresholds)):
    if i == 0:
        continue                                        # skip index 0, as the loop does
    n = n + len(detection_list)
print('detections:', n)

Compare n, the labels and the per-class scores with your golden output, and draw the boxes on the frame once and look at them. Capture the test frame the way production does. If the line is hardware-triggered, the hardware trigger grab loop in Python shows the acquire-and-save pattern to copy. Hold exposure and gain fixed across the comparison; setting camera exposure time through a GenICam API in Python is the pattern for pinning those values in code instead of in a menu.

When count and labels match, restore the loop. When they do not, the signature still does not match: go back to the reference before touching anything else. A scale error fails the single-frame test immediately, because nothing passes; subtler drifts show up in the first 20 production cycles, so watch those too.

How do you stop the next update from breaking production?

Record versions, control timing and keep a rollback. Write the firmware and module versions into the script header, update cameras only between production runs, and keep the previous firmware image on file so undoing a bad update is one reflash rather than a debug session.

Put the version strings where the next person reads them: firmware build, camera library version, interpreter version and the model file's size in bytes, which your load line already computes. Scripting the check for new versions is fine; scripting firmware update checks against a FOTA API shows the pattern for networked devices, but keep the install itself manual, scheduled and staged. Treat the update the way the camera firmware update workflow from approval to readback describes: approve, test on a bench unit, roll to the line, and read the version back after the reflash. On a PC-side script, pin the library and pin one library version everywhere: one environment, recorded version numbers, no silent drift.

A five-minute checklist for every update

1. Save one golden frame per part family with its expected labels and scores, because the re-test needs a fixed reference.
2. Record firmware, library and interpreter versions plus the model file size in the script header, because version skew should be visible at a glance.
3. Read the release notes for the new build and search them for every module your script imports, because signature changes are listed, not discovered.
4. Keep the previous firmware image where your flashing tool can reach it, because rollback stays a five-minute job.
5. Update the bench camera first and pass the golden-frame test before the line, because production never meets an untested signature.

Where the script runs decides how much churn you own

A camera that runs inspection on the device pairs firmware, interpreter and model runtime in one tested image, so an update and a rollback are each a single reflash. If you are moving inspection on-device for exactly that reason, the camera finder filters the on-device models by resolution, shutter and lens mount.

When the script belongs on a PC, a vision controller dedicated to the cell keeps the interpreter and the SDK on your update schedule instead of the office PC's; vision controllers are sized by camera count, light channels and mounting.

When is the firmware update not the cause at all?

Check three things before you blame the firmware: that the script runs on the device and interpreter you think it does, that no stale compiled module from the old build is still imported, and that the model and label files on the device are the ones your script expects.

Environment impostors come first, because they produce the same symptom hours after the reflash, when a moved script, a fresh virtual environment or a second installed library lands on the machine. Comparing Python environments when a camera is not detected is that hunt step by step, and an ImportError from DLL bitness shows how a wrong-bitness library imitates a broken camera.

Stale compiled modules are second: clear the interpreter's compiled-module cache after any update and run once before you judge. Wrong files are third: print the model size your load line already computes and compare it with the trained file, and print how many lines the labels file yields against the class count you trained, because a stale labels file shifts every class index and gives each detection the right box with the wrong name. Only when all three come back clean is the firmware the cause, and then the fix is exactly the six sections above.

Size the next cell so updates stay boring: on-device smart cameras when you want one tested image per camera, a dedicated vision controller when you want the environment on your own schedule. If you want a second opinion on the split for your camera count and cycle time, tell us through the contact page. More repair walks like this one sit on the systems topic page.