objcls.py 文件源码

def predict(imagepath, target_x, target_y, name, model):
    if imagepath.startswith('http://') or imagepath.startswith('https://') or imagepath.startswith('ftp://'):
        response = requests.get(imagepath)
        img = Image.open(BytesIO(response.content))
        img = img.resize((target_x, target_y))
    else:
        if not os.path.exists(imagepath):
            raise Exception('Input image file does not exist')
        img = image.load_img(imagepath, target_size=(target_x, target_y))

    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x = processInputImage(name, x)
    preds = decodePrediction(name, model.predict(x))
    result = []
    for p in preds[0]:
        result.append({"synset": p[0], "text": p[1], "prediction": float("{0:.2f}".format((p[2] * 100)))})

    return json.loads(jsonpickle.encode(result, unpicklable=False))