Object Detection inference on images hosted on google drive

I’m trying to run an inference from an image working in colab and getting a 500 error.

infer on an image hosted elsewhere

print(model.predict(“/content/drive/MyDrive/YOLOv5 Images/IMG_0309.JPG”, hosted=True, confidence=40, overlap=30).json())

Am I missing something obvious or is there a workaround to be able to pull images from Google Drive.

The error you’re encountering might be due to the fact that the image you’re trying to infer is not publicly accessible. The model.predict() function with hosted=True is designed to work with images hosted on a server that are publicly accessible via a URL.

If you’re working in Google Colab and your image is stored in Google Drive, you might need to read the image into memory first, and then pass it to the model.predict() function. Unfortunately, the extracts provided do not contain the exact code to do this.

However, you can try the following approach:

  1. Mount your Google Drive to the Colab notebook.
  2. Read the image into memory using a library like OpenCV or PIL.
  3. Pass the image data to the model.predict() function.

Here’s a rough example of how you might do this:

from google.colab import drive

from PIL import Image
import numpy as np

# Open the image file
img = Image.open('/content/drive/MyDrive/YOLOv5 Images/IMG_0309.JPG')
# Convert the image to numpy array
img_array = np.array(img)

# Now pass this array to model.predict
print(model.predict(img_array, confidence=40, overlap=30).json())

Please note that this is a general approach and might need adjustments based on your exact setup and the Roboflow library’s requirements. For more detailed instructions, you might want to refer to the Roboflow Documentation.

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Do I need to do anything different when I’m running inference on an image with more than one object I need bounding boxes for.