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| import streamlit as st | |
| import os, sys, io | |
| import urllib.request as urllib | |
| import numpy as np | |
| from PIL import Image | |
| import DPT | |
| ### Some Utils Functions ### | |
| def get_image(st_asset = st.sidebar, as_np_arr = False, extension_list = ['jpg', 'jpeg', 'png']): | |
| image_url, image_fh = None, None | |
| if st_asset.checkbox('use image URL?'): | |
| image_url = st_asset.text_input("Enter Image URL") | |
| else: | |
| image_fh = st_asset.file_uploader(label = "Update your image", type = extension_list) | |
| im = None | |
| if image_url: | |
| response = urllib.urlopen(image_url) | |
| im = Image.open(io.BytesIO(bytearray(response.read()))) | |
| elif image_fh: | |
| im = Image.open(image_fh) | |
| if im and as_np_arr: | |
| im = np.array(im) | |
| return im | |
| def show_miro_logo(use_column_width = False, width = 100, st_asset= st.sidebar): | |
| logo_url = 'https://miro.medium.com/max/1400/0*qLL-32srlq6Y_iTm.png' | |
| st_asset.image(logo_url, use_column_width = use_column_width, channels = 'BGR', output_format = 'PNG', width = width) | |
| def im_draw_bbox(pil_im, x0, y0, x1, y1, color = 'black', width = 3, caption = None, | |
| bbv_label_only = False): | |
| ''' | |
| draw bounding box on the input image pil_im in-place | |
| Args: | |
| color: color name as read by Pillow.ImageColor | |
| use_bbv: use bbox_visualizer | |
| ''' | |
| import bbox_visualizer as bbv | |
| if any([type(i)== float for i in [x0,y0,x1,y1]]): | |
| warnings.warn(f'im_draw_bbox: at least one of x0,y0,x1,y1 is of the type float and is converted to int.') | |
| x0 = int(x0) | |
| y0 = int(y0) | |
| x1 = int(x1) | |
| y1 = int(y1) | |
| if bbv_label_only: | |
| if caption: | |
| im_array = bbv.draw_flag_with_label(np.array(pil_im), | |
| label = caption, | |
| bbox = [x0,y0,x1,y1], | |
| line_color = ImageColor.getrgb(color), | |
| text_bg_color = ImageColor.getrgb(color) | |
| ) | |
| else: | |
| raise ValueError(f'im_draw_bbox: bbv_label_only is True but caption is None') | |
| else: | |
| im_array = bbv.draw_rectangle(np.array(pil_im), | |
| bbox = [x0, y0, x1, y1], | |
| bbox_color = ImageColor.getrgb(color), | |
| thickness = width | |
| ) | |
| im_array = bbv.add_label( | |
| im_array, label = caption, | |
| bbox = [x0,y0,x1,y1], | |
| text_bg_color = ImageColor.getrgb(color) | |
| )if caption else im_array | |
| return Image.fromarray(im_array) | |
| ### Streamlit App ### | |
| def mod_DPT(pil_im, model_def): | |
| depth_im = DPT.inference(img_array_rgb = np.array(pil_im), model_def = model_def) | |
| return depth_im | |
| def Main(model_dict): | |
| st.set_page_config(layout = 'wide') | |
| l_col, r_col = st.columns(2) | |
| show_miro_logo(st_asset = l_col) | |
| with l_col.expander('Monocular Depth: CNN vs Transformers'): | |
| st.info(f''' | |
| Comparsion of two models: [BTS (CNN)](https://github.com/ErenBalatkan/Bts-PyTorch) | |
| and [DPT (Transformer)](https://huggingface.co/Intel/dpt-large) | |
| ''') | |
| im = get_image(st_asset = r_col.expander('Input Image', expanded = True), extension_list = ['jpg','jpeg']) | |
| model_name = r_col.selectbox('Pick Model', options = ['DPT','BTS']) | |
| if im: | |
| model_def = DPT.load_model() | |
| d_im = mod_DPT(pil_im = im, model_def=model_def) | |
| l_col, r_col = st.columns(2) | |
| l_col.image(im, caption = 'Input Image') | |
| r_col.image(saliency_im, caption = 'Depth Map') | |
| else: | |
| st.warning(f'please provide an image :point_up:') | |
| if __name__ == '__main__': | |
| model_dict = load_model() | |
| Main(model_dict = model_dict) | |