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| 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 = "inception_v3" | |
| 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="inception_v3" | |
| description="Gradio Demo for inception_v3: [TF2] Imagenet (ILSVRC-2012-CLS) classification with Inception V3. To use it, simply upload your image or click on one of the examples to load them. Read more at the links below" | |
| article = "<p style='text-align: center'><a href='https://tfhub.dev/google/imagenet/mobilenet_v3_large_075_224/classification/5' target='_blank'>Tensorflow Hub</a></p>" | |
| examples=[['apple1.jpg']] | |
| gr.Interface(inference,gr.inputs.Image(type="filepath"),"label",title=title,description=description,article=article,examples=examples).launch(enable_queue=True) | |