cnn.py

def cnn_detect_object(path):
    args = { "prototxt": "cnn/bvlc_googlenet.prototxt",
        "model": "cnn/bvlc_googlenet.caffemodel",
        "labels": "cnn/synset_words.txt"}

    # load the input image from disk
    image = cv2.imread(path)

    # load the class labels from disk
    rows = open(args["labels"]).read().strip().split("\n")
    classes = [r[r.find(" ") + 1:].split(",")[0] for r in rows]

    # our CNN requires fixed spatial dimensions for our input image(s)
    # so we need to ensure it is resized to 224x224 pixels while
    # performing mean subtraction (104, 117, 123) to normalize the input;
    # after executing this command our "blob" now has the shape:
    # (1, 3, 224, 224)
    blob = cv2.dnn.blobFromImage(image, 1, (224, 224), (104, 117, 123))

    # load our serialized model from disk
    # print("[INFO] loading model...")
    net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])

    # set the blob as input to the network and perform a forward-pass to
    # obtain our output classification
    net.setInput(blob)
    preds = net.forward()
    # sort the indexes of the probabilities in descending order (higher
    # probabilitiy first) and grab the top-5 predictions
    idxs = np.argsort(preds[0])[::-1][:5]

    # loop over the top-5 predictions and display them
    result = []
    for (i, idx) in enumerate(idxs):
        # draw the top prediction on the input image
        result.append({"label": classes[idx], "probability": preds[0][idx]*100})

    # display the output image
    return result

loop.py

    def detect_objects(root_path, group, num):
        for i in range(1, num + 1):
            filename = "{}_{}_402_{}.jpg".format(root_path, group, i)
            result = rv.cnn_detect_object(filename)
            print('=====================')
            print("Image {} - most probable: {} : {}%".format(filename, result[0]["label"], result[0]["probability"]))
            print('Other options: ', end='')
            for item in result[1::]:
                print("{} ({:3.3}%), ".format(item["label"], item["probability"]), end='')
            print()

    root_path = "images/cnn_object_detection"

    print("Detecting successful images: ")
    detect_objects(root_path, "success", 5)
    print()

    print("Detecting failed images: ")
    detect_objects(root_path, "failed", 5)