import tensorflow as tf import tensorflow_hub as hub import requests from PIL import Image from io import BytesIO import matplotlib.pyplot as plt import numpy as np import gradio as gr #@title Helper functions for loading image (hidden) original_image_cache = {} def preprocess_image(image): image = np.array(image) # reshape into shape [batch_size, height, width, num_channels] img_reshaped = tf.reshape(image, [1, image.shape[0], image.shape[1], image.shape[2]]) # Use `convert_image_dtype` to convert to floats in the [0,1] range. image = tf.image.convert_image_dtype(img_reshaped, tf.float32) return image def load_image_from_url(img_url): """Returns an image with shape [1, height, width, num_channels].""" user_agent = {'User-agent': 'Colab Sample (https://tensorflow.org)'} response = requests.get(img_url, headers=user_agent) image = Image.open(BytesIO(response.content)) image = preprocess_image(image) return image def load_image(image_url, image_size=256, dynamic_size=False, max_dynamic_size=512): """Loads and preprocesses images.""" # Cache image file locally. if image_url in original_image_cache: img = original_image_cache[image_url] elif image_url.startswith('https://'): img = load_image_from_url(image_url) else: fd = tf.io.gfile.GFile(image_url, 'rb') img = preprocess_image(Image.open(fd)) original_image_cache[image_url] = img # Load and convert to float32 numpy array, add batch dimension, and normalize to range [0, 1]. img_raw = img if tf.reduce_max(img) > 1.0: img = img / 255. if len(img.shape) == 3: img = tf.stack([img, img, img], axis=-1) if not dynamic_size: img = tf.image.resize_with_pad(img, image_size, image_size) elif img.shape[1] > max_dynamic_size or img.shape[2] > max_dynamic_size: img = tf.image.resize_with_pad(img, max_dynamic_size, max_dynamic_size) return img, img_raw image_size = 224 dynamic_size = False model_name = "mobilenet_v2_100_224" model_handle_map = { "efficientnetv2-s": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_s/classification/2", "efficientnetv2-m": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_m/classification/2", "efficientnetv2-l": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_l/classification/2", "efficientnetv2-s-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_s/classification/2", "efficientnetv2-m-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_m/classification/2", "efficientnetv2-l-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_l/classification/2", "efficientnetv2-xl-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_xl/classification/2", "efficientnetv2-b0-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b0/classification/2", "efficientnetv2-b1-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b1/classification/2", "efficientnetv2-b2-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b2/classification/2", "efficientnetv2-b3-21k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_b3/classification/2", "efficientnetv2-s-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_s/classification/2", "efficientnetv2-m-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_m/classification/2", "efficientnetv2-l-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_l/classification/2", "efficientnetv2-xl-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_xl/classification/2", "efficientnetv2-b0-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b0/classification/2", "efficientnetv2-b1-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b1/classification/2", "efficientnetv2-b2-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b2/classification/2", "efficientnetv2-b3-21k-ft1k": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet21k_ft1k_b3/classification/2", "efficientnetv2-b0": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b0/classification/2", "efficientnetv2-b1": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b1/classification/2", "efficientnetv2-b2": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b2/classification/2", "efficientnetv2-b3": "https://tfhub.dev/google/imagenet/efficientnet_v2_imagenet1k_b3/classification/2", "efficientnet_b0": "https://tfhub.dev/tensorflow/efficientnet/b0/classification/1", "efficientnet_b1": "https://tfhub.dev/tensorflow/efficientnet/b1/classification/1", "efficientnet_b2": "https://tfhub.dev/tensorflow/efficientnet/b2/classification/1", "efficientnet_b3": "https://tfhub.dev/tensorflow/efficientnet/b3/classification/1", "efficientnet_b4": "https://tfhub.dev/tensorflow/efficientnet/b4/classification/1", "efficientnet_b5": "https://tfhub.dev/tensorflow/efficientnet/b5/classification/1", "efficientnet_b6": "https://tfhub.dev/tensorflow/efficientnet/b6/classification/1", "efficientnet_b7": "https://tfhub.dev/tensorflow/efficientnet/b7/classification/1", "bit_s-r50x1": "https://tfhub.dev/google/bit/s-r50x1/ilsvrc2012_classification/1", "inception_v3": "https://tfhub.dev/google/imagenet/inception_v3/classification/4", "inception_resnet_v2": "https://tfhub.dev/google/imagenet/inception_resnet_v2/classification/4", "resnet_v1_50": "https://tfhub.dev/google/imagenet/resnet_v1_50/classification/4", "resnet_v1_101": "https://tfhub.dev/google/imagenet/resnet_v1_101/classification/4", "resnet_v1_152": "https://tfhub.dev/google/imagenet/resnet_v1_152/classification/4", "resnet_v2_50": "https://tfhub.dev/google/imagenet/resnet_v2_50/classification/4", "resnet_v2_101": "https://tfhub.dev/google/imagenet/resnet_v2_101/classification/4", "resnet_v2_152": "https://tfhub.dev/google/imagenet/resnet_v2_152/classification/4", "nasnet_large": "https://tfhub.dev/google/imagenet/nasnet_large/classification/4", "nasnet_mobile": "https://tfhub.dev/google/imagenet/nasnet_mobile/classification/4", "pnasnet_large": "https://tfhub.dev/google/imagenet/pnasnet_large/classification/4", "mobilenet_v2_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_100_224/classification/4", "mobilenet_v2_130_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_130_224/classification/4", "mobilenet_v2_140_224": "https://tfhub.dev/google/imagenet/mobilenet_v2_140_224/classification/4", "mobilenet_v3_small_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_small_100_224/classification/5", "mobilenet_v3_small_075_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_small_075_224/classification/5", "mobilenet_v3_large_100_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_large_100_224/classification/5", "mobilenet_v3_large_075_224": "https://tfhub.dev/google/imagenet/mobilenet_v3_large_075_224/classification/5", } model_image_size_map = { "efficientnetv2-s": 384, "efficientnetv2-m": 480, "efficientnetv2-l": 480, "efficientnetv2-b0": 224, "efficientnetv2-b1": 240, "efficientnetv2-b2": 260, "efficientnetv2-b3": 300, "efficientnetv2-s-21k": 384, "efficientnetv2-m-21k": 480, "efficientnetv2-l-21k": 480, "efficientnetv2-xl-21k": 512, "efficientnetv2-b0-21k": 224, "efficientnetv2-b1-21k": 240, "efficientnetv2-b2-21k": 260, "efficientnetv2-b3-21k": 300, "efficientnetv2-s-21k-ft1k": 384, "efficientnetv2-m-21k-ft1k": 480, "efficientnetv2-l-21k-ft1k": 480, "efficientnetv2-xl-21k-ft1k": 512, "efficientnetv2-b0-21k-ft1k": 224, "efficientnetv2-b1-21k-ft1k": 240, "efficientnetv2-b2-21k-ft1k": 260, "efficientnetv2-b3-21k-ft1k": 300, "efficientnet_b0": 224, "efficientnet_b1": 240, "efficientnet_b2": 260, "efficientnet_b3": 300, "efficientnet_b4": 380, "efficientnet_b5": 456, "efficientnet_b6": 528, "efficientnet_b7": 600, "inception_v3": 299, "inception_resnet_v2": 299, "mobilenet_v2_100_224": 224, "mobilenet_v2_130_224": 224, "mobilenet_v2_140_224": 224, "nasnet_large": 331, "nasnet_mobile": 224, "pnasnet_large": 331, "resnet_v1_50": 224, "resnet_v1_101": 224, "resnet_v1_152": 224, "resnet_v2_50": 224, "resnet_v2_101": 224, "resnet_v2_152": 224, "mobilenet_v3_small_100_224": 224, "mobilenet_v3_small_075_224": 224, "mobilenet_v3_large_100_224": 224, "mobilenet_v3_large_075_224": 224, } model_handle = model_handle_map[model_name] max_dynamic_size = 512 if model_name in model_image_size_map: image_size = model_image_size_map[model_name] dynamic_size = False print(f"Images will be converted to {image_size}x{image_size}") else: dynamic_size = True print(f"Images will be capped to a max size of {max_dynamic_size}x{max_dynamic_size}") labels_file = "https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt" #download labels and creates a maps downloaded_file = tf.keras.utils.get_file("labels.txt", origin=labels_file) classes = [] with open(downloaded_file) as f: labels = f.readlines() classes = [l.strip() for l in labels] classifier = hub.load(model_handle) def inference(img): image, original_image = load_image(img, image_size, dynamic_size, max_dynamic_size) input_shape = image.shape warmup_input = tf.random.uniform(input_shape, 0, 1.0) warmup_logits = classifier(warmup_input).numpy() # Run model on image probabilities = tf.nn.softmax(classifier(image)).numpy() top_5 = tf.argsort(probabilities, axis=-1, direction="DESCENDING")[0][:5].numpy() np_classes = np.array(classes) # Some models include an additional 'background' class in the predictions, so # we must account for this when reading the class labels. includes_background_class = probabilities.shape[1] == 1001 result = {} for i, item in enumerate(top_5): class_index = item if includes_background_class else item + 1 line = f'({i+1}) {class_index:4} - {classes[class_index]}: {probabilities[0][top_5][i]}' result[classes[class_index]] = probabilities[0][top_5][i].item() return result title="mobilenet_v2_100_224" description="Gradio Demo for mobilenet_v2_100_224: Imagenet (ILSVRC-2012-CLS) classification with MobileNet V2 (depth multiplier 1.00). To use it, simply upload your image or click on one of the examples to load them. Read more at the links below" article = "

Tensorflow Hub

" examples=[['apple1.jpg']] gr.Interface(inference,gr.inputs.Image(type="filepath"),"label",title=title,description=description,article=article,examples=examples).launch(enable_queue=True)