Using trained webcam on trained roboflow model

Question

I'm trying to run a trained roboflow model using my webcam on visual code studio. The webcam does load up alongside the popup, but it's just a tiny rectangle in the corner and you can't see anything else. If i change "image", image to "image",1 or something else in the cv2.imshow line, the webcam lights up for a second and returns the error code:
cv2.error: OpenCV(4.5.4) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\color.cpp:182: error: (-215:Assertion failed) !_src.empty() in function 'cv::cvtColor'

Here is my code as obtained from a github roboflow has:

# load config
import json
with open('roboflow_config.json') as f:
    config = json.load(f)

    ROBOFLOW_API_KEY = "********"
    ROBOFLOW_MODEL = "penguins-ojf2k"
    ROBOFLOW_SIZE = "416"

    FRAMERATE = config["FRAMERATE"]
    BUFFER = config["BUFFER"]

import asyncio
import cv2
import base64
import numpy as np
import httpx
import time

# Construct the Roboflow Infer URL
# (if running locally replace https://detect.roboflow.com/ with eg http://127.0.0.1:9001/)
upload_url = "".join([
    "https://detect.roboflow.com/",
    ROBOFLOW_MODEL,
    "?api_key=",
    ROBOFLOW_API_KEY,
    "&format=image", # Change to json if you want the prediction boxes, not the visualization
    "&stroke=5"
])

# Get webcam interface via opencv-python
video = cv2.VideoCapture(0,cv2.CAP_DSHOW)

# Infer via the Roboflow Infer API and return the result
# Takes an httpx.AsyncClient as a parameter
async def infer(requests):
    # Get the current image from the webcam
    ret, img = video.read()
   
    # Resize (while maintaining the aspect ratio) to improve speed and save bandwidth
    height, width, channels = img.shape
    scale = min(height, width)
    img = cv2.resize(img, (2000, 1500))

    # Encode image to base64 string
    retval, buffer = cv2.imencode('.jpg', img)
    img_str = base64.b64encode(buffer)

    # Get prediction from Roboflow Infer API
    resp = await requests.post(upload_url, data=img_str, headers={
        "Content-Type": "application/x-www-form-urlencoded"
    })


    # Parse result image
    image = np.asarray(bytearray(resp.content), dtype="uint8")
    image = cv2.imdecode(image, cv2.IMREAD_COLOR)

    return image


# Main loop; infers at FRAMERATE frames per second until you press "q"
async def main():
    # Initialize
    last_frame = time.time()

    # Initialize a buffer of images
    futures = []

    async with httpx.AsyncClient() as requests:
        while True:
            
            # On "q" keypress, exit
            if(cv2.waitKey(1) == ord('q')):
                break

            # Throttle to FRAMERATE fps and print actual frames per second achieved
            elapsed = time.time() - last_frame
            await asyncio.sleep(max(0, 1/FRAMERATE - elapsed))
            print((1/(time.time()-last_frame)), " fps")
            last_frame = time.time()

            # Enqueue the inference request and safe it to our buffer
            task = asyncio.create_task(infer(requests))
            futures.append(task)

            # Wait until our buffer is big enough before we start displaying results
            if len(futures) < BUFFER * FRAMERATE:
                continue

            # Remove the first image from our buffer
            # wait for it to finish loading (if necessary)
            image = await futures.pop(0)
            # And display the inference results
            img = cv2.imread('img.jpg')
            cv2.imshow('image', image)
            
            
            
# Run our main loop
asyncio.set_event_loop_policy(asyncio.WindowsSelectorEventLoopPolicy())
asyncio.run(main())


# Release resources when finished
video.release()
cv2.destroyAllWindows()

Answer 1

It looks like you're missing your model's version number so the API is probably returning a 404 error which OpenCV is trying to read as an image.

I found your project on Roboflow Universe based on the ROBOFLOW_MODEL in your code; it looks like you're looking for version 3.

So try changing the line

ROBOFLOW_MODEL = "penguins-ojf2k"

to

ROBOFLOW_MODEL = "penguins-ojf2k/3"

It also looks like your model was trained at 640x640 (not 416x416) so you should change ROBOFLOW_SIZE to 640 as well for best results.