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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 | |
| def greet(name): | |
| return "Hello " + name + "!!" | |
| #iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
| #iface.launch() | |
| 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() | |
| saved_model_dir = os.path.join(download_dir, "saved_model.pb") | |
| detection_model = tf.saved_model.load(saved_model_dir) | |
| return detection_model | |
| def predict(pilimg): | |
| image_np = pil_image_as_numpy_array(pilimg) | |
| return predict2(image_np) | |
| 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",label="Input Image"),gr.Textbox(placeholder="0.50",label="Set the confidence threshold (0.00-1.00)")], | |
| outputs=gr.Image(type="pil",label="Output Image"), | |
| title="Cauliflower and Beetroot Detection", | |
| description="Model: ssd_resnet50_v1_fpn_640x640_coco17_tpu-8", | |
| theme=gr.themes.Soft(primary_hue="blue", secondary_hue="sky"), | |
| examples=[["test_samples/image489.png",0.55], ["test_samples/image825.png",0.55], ["test_samples/image833.png",0.55], ["test_samples/image846.png",0.55]] | |
| ).launch(share=True) | |