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Configuration error
Configuration error
| import pathlib | |
| import validators | |
| import requests | |
| import gradio as gr | |
| # For running inference on the TF-Hub module. | |
| import tensorflow as tf | |
| # For downloading the image. | |
| # For drawing onto the image. | |
| import numpy as np | |
| from PIL import Image | |
| from PIL import ImageColor | |
| from PIL import ImageDraw | |
| from PIL import ImageFont | |
| print("load model...") | |
| detector = tf.saved_model.load("model/saved_model") | |
| def draw_bounding_box_on_image(image, | |
| ymin, | |
| xmin, | |
| ymax, | |
| xmax, | |
| color, | |
| font, | |
| thickness=4, | |
| display_str_list=()): | |
| """Adds a bounding box to an image.""" | |
| draw = ImageDraw.Draw(image) | |
| im_width, im_height = image.size | |
| (left, right, top, bottom) = (xmin * im_width, xmax * im_width, | |
| ymin * im_height, ymax * im_height) | |
| draw.line([(left, top), (left, bottom), (right, bottom), (right, top), | |
| (left, top)], | |
| width=thickness, | |
| fill=color) | |
| # If the total height of the display strings added to the top of the bounding | |
| # box exceeds the top of the image, stack the strings below the bounding box | |
| # instead of above. | |
| display_str_heights = [font.getsize(ds)[1] for ds in display_str_list] | |
| # Each display_str has a top and bottom margin of 0.05x. | |
| total_display_str_height = (1 + 2 * 0.05) * sum(display_str_heights) | |
| if top > total_display_str_height: | |
| text_bottom = top | |
| else: | |
| text_bottom = top + total_display_str_height | |
| # Reverse list and print from bottom to top. | |
| for display_str in display_str_list[::-1]: | |
| text_width, text_height = font.getsize(display_str) | |
| margin = np.ceil(0.05 * text_height) | |
| draw.rectangle([(left, text_bottom - text_height - 2 * margin), | |
| (left + text_width, text_bottom)], | |
| fill=color) | |
| draw.text((left + margin, text_bottom - text_height - margin), | |
| display_str, | |
| fill="black", | |
| font=font) | |
| text_bottom -= text_height - 2 * margin | |
| """Overlay labeled boxes on an image with formatted scores and label names.""" | |
| def draw_boxes(image, boxes, class_names, scores, max_boxes=10, min_score=0.1): | |
| colors = list(ImageColor.colormap.values()) | |
| try: | |
| font = ImageFont.truetype("/usr/share/fonts/truetype/liberation/LiberationSansNarrow-Regular.ttf", | |
| 25) | |
| except IOError: | |
| print("Font not found, using default font.") | |
| font = ImageFont.load_default() | |
| for i in range(min(boxes.shape[1], max_boxes)): | |
| if scores[0][i] >= min_score: | |
| ymin, xmin, ymax, xmax = tuple(boxes[0][i]) | |
| display_str = "{}: {}%".format(class_names[i], | |
| int(100 * scores[0][i])) | |
| color = colors[hash(class_names[i]) % len(colors)] | |
| image_pil = Image.fromarray(np.uint8(image)).convert("RGB") | |
| draw_bounding_box_on_image( | |
| image_pil, | |
| ymin, | |
| xmin, | |
| ymax, | |
| xmax, | |
| color, | |
| font, | |
| display_str_list=[display_str]) | |
| np.copyto(image, np.array(image_pil)) | |
| return image | |
| def run_detector(url_input, image_input, minscore=0.1): | |
| if (validators.url(url_input)): | |
| img = Image.open(requests.get(url_input, stream=True).raw) | |
| elif (image_input): | |
| img = image_input | |
| converted_img = tf.image.convert_image_dtype(img, tf.uint8)[ | |
| tf.newaxis, ...] | |
| result = detector(converted_img) | |
| result = {key: value.numpy() for key, value in result.items()} | |
| labels = ["cyclist" for _ in range(len(result["detection_scores"][0]))] | |
| image_with_boxes = draw_boxes( | |
| np.array(img), result["detection_boxes"], | |
| labels, result["detection_scores"], min_score=minscore) | |
| return image_with_boxes | |
| css = ''' | |
| h1#title { | |
| text-align: center; | |
| } | |
| ''' | |
| demo = gr.Blocks(css=css) | |
| title = """<h1 id="title">Custom Cyclists detector</h1>""" | |
| description = "todo" | |
| def set_example_image(example: list) -> dict: | |
| return gr.Image.update(value=example[0]) | |
| def set_example_url(example: list) -> dict: | |
| return gr.Textbox.update(value=example[0]) | |
| urls = ["https://hips.hearstapps.com/hmg-prod.s3.amazonaws.com/images/cyclist-on-path-by-sea-royalty-free-image-1656931301.jpg?crop=0.727xw:0.699xh;0.134xw,0.169xh&resize=640:*"] | |
| with demo: | |
| gr.Markdown(title) | |
| gr.Markdown(description) | |
| slider_input = gr.Slider(minimum=0.0, maximum=1, | |
| value=0.2, label='Prediction Threshold') | |
| with gr.Tabs(): | |
| with gr.TabItem('Image URL'): | |
| with gr.Row(): | |
| url_input = gr.Textbox( | |
| lines=2, label='Enter valid image URL here..') | |
| img_output_from_url = gr.Image(shape=(640, 640)) | |
| with gr.Row(): | |
| example_url = gr.Dataset(components=[url_input], samples=[ | |
| [str(url)] for url in urls]) | |
| url_but = gr.Button('Detect') | |
| with gr.TabItem('Image Upload'): | |
| with gr.Row(): | |
| img_input = gr.Image(type='pil') | |
| img_output_from_upload = gr.Image(shape=(650, 650)) | |
| with gr.Row(): | |
| example_images = gr.Dataset(components=[img_input], | |
| samples=[[path.as_posix()] | |
| for path in sorted(pathlib.Path('images').rglob('*.jpg'))]) | |
| img_but = gr.Button('Detect') | |
| url_but.click(run_detector, inputs=[ | |
| url_input, img_input, slider_input], outputs=img_output_from_url, queue=True) | |
| img_but.click(run_detector, inputs=[ | |
| url_input, img_input, slider_input], outputs=img_output_from_upload, queue=True) | |
| example_images.click(fn=set_example_image, inputs=[ | |
| example_images], outputs=[img_input]) | |
| example_url.click(fn=set_example_url, inputs=[ | |
| example_url], outputs=[url_input]) | |
| demo.launch(enable_queue=True) | |