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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 | |
| 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 | |
| def detect_video(video): | |
| # Create a video capture object | |
| cap = cv2.VideoCapture(video) | |
| # Process frames in a loop | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| if not ret: | |
| break | |
| # Expand dimensions since model expects images to have shape: [1, None, None, 3] | |
| image_np_expanded = np.expand_dims(frame, axis=0) | |
| # Run inference | |
| output_dict = detection_model(image_np_expanded) | |
| # Extract detections | |
| boxes = output_dict['detection_boxes'][0].numpy() | |
| scores = output_dict['detection_scores'][0].numpy() | |
| classes = output_dict['detection_classes'][0].numpy().astype(np.int64) | |
| # Draw bounding boxes and labels | |
| image_np_with_detections = viz_utils.visualize_boxes_and_labels_on_image_array( | |
| frame, | |
| boxes, | |
| classes, | |
| scores, | |
| category_index, | |
| use_normalized_coordinates=True, | |
| max_boxes_to_draw=20, | |
| min_score_thresh=.5, | |
| agnostic_mode=False) | |
| # Yield the processed frame | |
| yield image_np_with_detections | |
| # Release resources | |
| cap.release() | |
| REPO_ID = "apailang/mytfodmodel" | |
| 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"), | |
| # title="Image Prediction Interface", | |
| # description="Upload a Image for prediction", | |
| # ).launch(share=True) | |
| # Second interface for video prediction | |
| video_interface = gr.Interface( | |
| fn=detect_video, | |
| inputs=gr.Video(interactive=True), | |
| outputs=gr.Video(), | |
| title="Video Prediction Interface", | |
| description="Upload a video for prediction", | |
| live=True | |
| ) | |
| # Launch both interfaces | |
| #image_interface.launch(share=True) | |
| video_interface.launch(share=True) | |