Using trained webcam on trained roboflow model - webcam

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.