Edge Impulse - Classify all images in a directory

classify-directory.py

#!/usr/bin/env python

import device_patches       # Device specific patches for Jetson Nano (needs to be before importing cv2)

import cv2
import os
import sys, getopt
import numpy as np
from edge_impulse_linux.image import ImageImpulseRunner

runner = None

def help():
    print('python classify-image.py <path_to_model.eim> <path-to-directory/>')

def main(argv):
    try:
        opts, args = getopt.getopt(argv, "h", ["--help"])
    except getopt.GetoptError:
        help()
        sys.exit(2)

    for opt, arg in opts:
        if opt in ('-h', '--help'):
            help()
            sys.exit()

    if len(args) != 2:
        help()
        sys.exit(2)

    model = args[0]

    dir_path = os.path.dirname(os.path.realpath(__file__))
    modelfile = os.path.join(dir_path, model)

    print('MODEL: ' + modelfile)

    with ImageImpulseRunner(modelfile) as runner:
        try:
            model_info = runner.init()
            print('Loaded runner for "' + model_info['project']['owner'] + ' / ' + model_info['project']['name'] + '"')
            labels = model_info['model_parameters']['labels']

            # Create a list of all files in the directory (ignore subdirectories)
            dirpath = args[1]
            filepaths = [os.path.join(dirpath, f) for f in os.listdir(dirpath) if os.path.isfile(os.path.join(dirpath, f))]

            # Perform inference on all files
            for filepath in filepaths:

                # Open image (skip if not an image)
                print(f"Performing inference on {filepath}")
                img = cv2.imread(filepath)
                if img is None:
                    print(f"Failed to load image: {filepath}")
                    continue

                # imread returns images in BGR format, so we need to convert to RGB
                img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

                # get_features_from_image also takes a crop direction arguments in case you don't have square images
                features, cropped = runner.get_features_from_image(img)

                # Do inference
                res = runner.classify(features)

                # Print results
                if "classification" in res["result"].keys():
                    print('Result (%d ms.) ' % (res['timing']['dsp'] + res['timing']['classification']), end='')
                    for label in labels:
                        score = res['result']['classification'][label]
                        print('%s: %.2f\t' % (label, score), end='')
                    print('', flush=True)
                elif "bounding_boxes" in res["result"].keys():
                    print('Found %d bounding boxes (%d ms.)' % (len(res["result"]["bounding_boxes"]), res['timing']['dsp'] + res['timing']['classification']))
                    for bb in res["result"]["bounding_boxes"]:
                        print('\t%s (%.2f): x=%d y=%d w=%d h=%d' % (bb['label'], bb['value'], bb['x'], bb['y'], bb['width'], bb['height']))
                        cropped = cv2.rectangle(cropped, (bb['x'], bb['y']), (bb['x'] + bb['width'], bb['y'] + bb['height']), (255, 0, 0), 1)

            # the image will be resized and cropped, save a copy of the picture here
            # so you can see what's being passed into the classifier
            #cv2.imwrite('debug.jpg', cv2.cvtColor(cropped, cv2.COLOR_RGB2BGR))

        finally:
            if (runner):
                runner.stop()

if __name__ == "__main__":
   main(sys.argv[1:])