SubbaRaoKota commited on
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  1. README.md +4 -3
  2. app.py +91 -0
  3. data/label_map.pbtxt +8 -0
  4. requirements.txt +6 -0
  5. test_samples/Image_64.jpg +0 -0
README.md CHANGED
@@ -1,10 +1,11 @@
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  ---
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  title: 23B060P
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  emoji: 🐢
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- colorFrom: purple
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- colorTo: purple
 
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  sdk: gradio
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- sdk_version: 4.15.0
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  app_file: app.py
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  pinned: false
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  license: apache-2.0
 
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  ---
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  title: 23B060P
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  emoji: 🐢
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+ colorFrom: indigo
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+ colorTo: red
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+ python_version: 3.8
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  sdk: gradio
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+ sdk_version: 4.0.2
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  app_file: app.py
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  pinned: false
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  license: apache-2.0
app.py ADDED
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+ import matplotlib.pyplot as plt
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+ import numpy as np
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+ from six import BytesIO
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+ from PIL import Image
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+ import tensorflow as tf
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+ from object_detection.utils import label_map_util
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+ from object_detection.utils import visualization_utils as viz_utils
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+ from object_detection.utils import ops as utils_op
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+ import tarfile
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+ import wget
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+ import gradio as gr
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+ from huggingface_hub import snapshot_download
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+ import os
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+
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+ PATH_TO_LABELS = 'data/label_map.pbtxt'
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+ category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
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+
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+ def pil_image_as_numpy_array(pilimg):
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+
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+ img_array = tf.keras.utils.img_to_array(pilimg)
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+ img_array = np.expand_dims(img_array, axis=0)
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+ return img_array
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+
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+ def load_image_into_numpy_array(path):
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+
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+ image = None
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+ image_data = tf.io.gfile.GFile(path, 'rb').read()
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+ image = Image.open(BytesIO(image_data))
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+ return pil_image_as_numpy_array(image)
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+
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+ def load_model():
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+ download_dir = snapshot_download(REPO_ID)
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+ saved_model_dir = os.path.join(download_dir, "saved_model")
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+ detection_model = tf.saved_model.load(saved_model_dir)
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+ return detection_model
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+
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+ def load_model2():
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+ wget.download("https://nyp-aicourse.s3-ap-southeast-1.amazonaws.com/pretrained-models/balloon_model.tar.gz")
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+ tarfile.open("balloon_model.tar.gz").extractall()
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+ model_dir = 'saved_model'
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+ detection_model = tf.saved_model.load(str(model_dir))
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+ return detection_model
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+
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+ # samples_folder = 'test_samples
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+ # image_path = 'test_samples/sample_balloon.jpeg
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+ #
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+
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+ def predict(pilimg):
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+
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+ image_np = pil_image_as_numpy_array(pilimg)
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+ return predict2(image_np)
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+
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+ def predict2(image_np):
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+
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+ results = detection_model(image_np)
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+
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+ # different object detection models have additional results
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+ result = {key:value.numpy() for key,value in results.items()}
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+
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+ label_id_offset = 0
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+ image_np_with_detections = image_np.copy()
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+
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+ viz_utils.visualize_boxes_and_labels_on_image_array(
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+ image_np_with_detections[0],
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+ result['detection_boxes'][0],
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+ (result['detection_classes'][0] + label_id_offset).astype(int),
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+ result['detection_scores'][0],
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+ category_index,
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+ use_normalized_coordinates=True,
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+ max_boxes_to_draw=200,
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+ min_score_thresh=.60,
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+ agnostic_mode=False,
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+ line_thickness=2)
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+
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+ result_pil_img = tf.keras.utils.array_to_img(image_np_with_detections[0])
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+
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+ return result_pil_img
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+
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+
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+ REPO_ID = "SubbaRaoKota/myobjmodel"
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+ detection_model = load_model()
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+ # pil_image = Image.open(image_path)
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+ # image_arr = pil_image_as_numpy_array(pil_image)
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+
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+ # predicted_img = predict(image_arr)
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+ # predicted_img.save('predicted.jpg')
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+
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+ gr.Interface(fn=predict,
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+ inputs=gr.Image(type="pil"),
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+ outputs=gr.Image(type="pil")
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+ ).launch(share=True)
data/label_map.pbtxt ADDED
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+ item {
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+ id: 1
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+ name: 'burger'
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+ }
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+ item {
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+ id: 2
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+ name: 'fries'
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+ }
requirements.txt ADDED
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+ #tf2-tensorflow-object-detection-api
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+ tf-models-research-object-detection
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+ matplotlib
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+ wget
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+ Pillow==9.5
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+ huggingface_hub
test_samples/Image_64.jpg ADDED