infer-simple.py

# load config
import cv2
import base64
import numpy as np
import requests
import time
from dotenv import load_dotenv
import os
# Load key-value pairs from .env file
load_dotenv()
# Construct the Roboflow Infer URL
# (if running locally replace https://detect.roboflow.com/ with eg http://127.0.0.1:9001/)
ROBOFLOW_MODEL = os.getenv("ROBOFLOW_MODEL")
ROBOFLOW_VERSION = os.getenv("ROBOFLOW_VERSION")
ROBOFLOW_API_KEY = os.getenv("ROBOFLOW_API_KEY")
ROBOFLOW_SIZE = 416
FRAMERATE = 24
BUFFER = 0.5

upload_url = "".join([
    "https://detect.roboflow.com/",
    ROBOFLOW_MODEL,
    "/",
    ROBOFLOW_VERSION,
    "?api_key=",
    ROBOFLOW_API_KEY,
    "&format=image",
    "&stroke=8",
    "&confidence=30"
])
# Get webcam interface via opencv-python
video = cv2.VideoCapture(0)

# Infer via the Roboflow Infer API and return the result
def infer():
    # Get the current image from the webcam
    ret, img = video.read()
    cv2.imshow('img', img)

    # Resize (while maintaining the aspect ratio) to improve speed and save bandwidth
    height, width, channels = img.shape
    scale = ROBOFLOW_SIZE / max(height, width)
    img = cv2.resize(img, (round(scale * width), round(scale * height)))

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

    # Get prediction from Roboflow Infer API
    resp = requests.post(upload_url, data=img_str, headers={
        "Content-Type": "application/x-www-form-urlencoded"
    }, stream=True).raw
    
    # Parse result image
    image = np.asarray(bytearray(resp.read()), dtype="uint8")
    image = cv2.imdecode(image, cv2.IMREAD_COLOR)

    return image

# Main loop; infers sequentially until you press "q"
while 1:
    # On "q" keypress, exit
    if(cv2.waitKey(1) == ord('q')):
        break

    # Capture start time to calculate fps
    start = time.time()

    # Synchronously get a prediction from the Roboflow Infer API
    image = infer()
    # And display the inference results
    if(image is None):
        continue
    cv2.imshow('image', image)

    # Print frames per second
    print((1/(time.time()-start)), " fps")

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