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| import matplotlib.pyplot as plt | |
| import numpy as np | |
| from six import BytesIO | |
| from PIL import Image | |
| import tensorflow as tf | |
| from object_detection.utils import label_map_util | |
| from object_detection.utils import visualization_utils as viz_utils | |
| from object_detection.utils import ops as utils_op | |
| import tarfile | |
| import wget | |
| import gradio as gr | |
| from huggingface_hub import snapshot_download | |
| import os | |
| # Install TensorFlow within the Hugging Face environment | |
| os.system('pip install tensorflow') | |
| # Now you can import TensorFlow | |
| import tensorflow as tf | |
| PATH_TO_LABELS = 'data/label_map.pbtxt' | |
| category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True) | |
| def pil_image_as_numpy_array(pilimg): | |
| img_array = tf.keras.utils.img_to_array(pilimg) | |
| img_array = np.expand_dims(img_array, axis=0) | |
| return img_array | |
| def load_image_into_numpy_array(path): | |
| image = None | |
| image_data = tf.io.gfile.GFile(path, 'rb').read() | |
| image = Image.open(BytesIO(image_data)) | |
| return pil_image_as_numpy_array(image) | |
| def load_model(): | |
| download_dir = snapshot_download(REPO_ID) | |
| saved_model_dir = os.path.join(download_dir, "saved_model") | |
| detection_model = tf.saved_model.load(saved_model_dir) | |
| return detection_model | |
| def load_model2(): | |
| wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz") | |
| tarfile.open("balloon_model.tar.gz").extractall() | |
| model_dir = 'saved_model' | |
| detection_model = tf.saved_model.load(str(model_dir)) | |
| return detection_model | |
| # samples_folder = 'test_samples | |
| # image_path = 'test_samples/sample_balloon.jpeg | |
| # | |
| def predict(pilimg): | |
| image_np = pil_image_as_numpy_array(pilimg) | |
| return predict2(image_np) | |
| def predict2(image_np): | |
| results = detection_model(image_np) | |
| # different object detection models have additional results | |
| result = {key:value.numpy() for key,value in results.items()} | |
| label_id_offset = 0 | |
| image_np_with_detections = image_np.copy() | |
| viz_utils.visualize_boxes_and_labels_on_image_array( | |
| image_np_with_detections[0], | |
| result['detection_boxes'][0], | |
| (result['detection_classes'][0] + label_id_offset).astype(int), | |
| result['detection_scores'][0], | |
| category_index, | |
| use_normalized_coordinates=True, | |
| max_boxes_to_draw=200, | |
| min_score_thresh=.60, | |
| agnostic_mode=False, | |
| line_thickness=2) | |
| result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0]) | |
| return result_pil_img | |
| REPO_ID = "Louisw3399/burgerorfriesdetector" | |
| detection_model = load_model() | |
| # pil_image = Image.open(image_path) | |
| # image_arr = pil_image_as_numpy_array(pil_image) | |
| # predicted_img = predict(image_arr) | |
| # predicted_img.save('predicted.jpg') | |
| gr.Interface(fn=predict, | |
| inputs=gr.Image(type="pil"), | |
| outputs=gr.Image(type="pil") | |
| ).launch(share=True) | |