[英]Using trained webcam on trained roboflow model
我正在嘗試在 visual code studio 上使用我的網絡攝像頭運行訓練有素的 roboflow model。 網絡攝像頭確實會與彈出窗口一起加載,但它只是角落里的一個小矩形,你看不到其他任何東西。 如果我將“圖像”、圖像更改為“圖像”、1 或 cv2.imshow 行中的其他內容,網絡攝像頭會亮起一秒鍾並返回錯誤代碼:
cv2.error: OpenCV(4.5.4) D:\a\opencv-python\opencv-python\opencv\modules\imgproc\src\color.cpp:182: 錯誤: (-215:斷言失敗)._src:empty () 在 function 'cv::cvtColor'
這是我從 github roboflow 獲得的代碼:
# 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()
看起來您缺少型號的版本號,因此 API 可能返回 404 錯誤,OpenCV 正試圖將其讀取為圖像。
我根據您代碼中的ROBOFLOW_MODEL
在Roboflow Universe 上找到了您的項目; 看起來您正在尋找版本3
。
所以嘗試改變線路
ROBOFLOW_MODEL = "penguins-ojf2k"
到
ROBOFLOW_MODEL = "penguins-ojf2k/3"
看起來你的 model 是在 640x640(不是 416x416)下訓練的,所以你也應該將ROBOFLOW_SIZE
更改為640
以獲得最佳結果。
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