import matplotlib.pyplot as plt import numpy as np from six import BytesIO from PIL import Image import tensorflow as tf import tarfile import wget import gradio as gr from huggingface_hub import snapshot_download import os 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 PATH_TO_LABELS = '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) # download_dir = os.path.join(download_dir, "saved_model") 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 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=0.60, agnostic_mode=False, line_thickness=3) result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0]) return result_pil_img REPO_ID = "A23066X/A23066X_model" 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') title = "Cauliflower and Beetroot Detection" description = "Using ssd_resnet50_v1_fpn_640x640_coco17_tpu-8" gr.Interface(fn=predict, title = title, description = description, css=css_code, inputs=gr.Image(type="pil", height=250), outputs=gr.Image(type="pil", height=250) ).launch(share=True)