from ultralytics import YOLO import cv2 import gradio as gr import numpy as np import os import torch import utils import plotly.graph_objects as go from image_segmenter import ImageSegmenter from monocular_depth_estimator import MonocularDepthEstimator from point_cloud_generator import display_pcd # params CANCEL_PROCESSING = False img_seg = ImageSegmenter(model_type="yolov8s-seg") depth_estimator = MonocularDepthEstimator(model_type="midas_v21_small_256") def process_image(image): image = utils.resize(image) image_segmentation, objects_data = img_seg.predict(image) depthmap, depth_colormap = depth_estimator.make_prediction(image) dist_image = utils.draw_depth_info(image, depthmap, objects_data) objs_pcd = utils.generate_obj_pcd(depthmap, objects_data) plot_fig = display_pcd(objs_pcd) return image_segmentation, depth_colormap, dist_image, plot_fig def test_process_img(image): image = utils.resize(image) image_segmentation, objects_data = img_seg.predict(image) depthmap, depth_colormap = depth_estimator.make_prediction(image) return image_segmentation, objects_data, depthmap, depth_colormap def process_video(vid_path=None): vid_cap = cv2.VideoCapture(vid_path) while vid_cap.isOpened(): ret, frame = vid_cap.read() if ret: print("making predictions ....") frame = utils.resize(frame) image_segmentation, objects_data = img_seg.predict(frame) depthmap, depth_colormap = depth_estimator.make_prediction(frame) dist_image = utils.draw_depth_info(frame, depthmap, objects_data) yield cv2.cvtColor(image_segmentation, cv2.COLOR_BGR2RGB), depth_colormap, cv2.cvtColor(dist_image, cv2.COLOR_BGR2RGB) return None def update_segmentation_options(options): img_seg.is_show_bounding_boxes = True if 'Show Boundary Box' in options else False img_seg.is_show_segmentation = True if 'Show Segmentation Region' in options else False img_seg.is_show_segmentation_boundary = True if 'Show Segmentation Boundary' in options else False def update_confidence_threshold(thres_val): img_seg.confidence_threshold = thres_val/100 def model_selector(model_type): if "Small - Better performance and less accuracy" == model_type: midas_model, yolo_model = "midas_v21_small_256", "yolov8s-seg" elif "Medium - Balanced performance and accuracy" == model_type: midas_model, yolo_model = "dpt_hybrid_384", "yolov8m-seg" elif "Large - Slow performance and high accuracy" == model_type: midas_model, yolo_model = "dpt_large_384", "yolov8l-seg" else: midas_model, yolo_model = "midas_v21_small_256", "yolov8s-seg" img_seg = ImageSegmenter(model_type=yolo_model) depth_estimator = MonocularDepthEstimator(model_type=midas_model) def cancel(): CANCEL_PROCESSING = True if __name__ == "__main__": # testing # img_1 = cv2.imread("assets/images/bus.jpg") # img_1 = utils.resize(img_1) # image_segmentation, objects_data, depthmap, depth_colormap = test_process_img(img_1) # final_image = utils.draw_depth_info(image_segmentation, depthmap, objects_data) # objs_pcd = utils.generate_obj_pcd(depthmap, objects_data) # # print(objs_pcd[0][0]) # display_pcd(objs_pcd, use_matplotlib=True) # cv2.imshow("Segmentation", image_segmentation) # cv2.imshow("Depth", depthmap*objects_data[2][3]) # cv2.imshow("Final", final_image) # cv2.waitKey(0) # cv2.destroyAllWindows() # gradio gui app with gr.Blocks() as my_app: # title gr.Markdown("

Simultaneous Segmentation and Depth Estimation

") gr.Markdown("

Created by Vaishanth

") gr.Markdown("

This model estimates the depth of segmented objects.

") # tabs with gr.Tab("Image"): with gr.Row(): with gr.Column(scale=1): img_input = gr.Image() model_type_img = gr.Dropdown( ["Small - Better performance and less accuracy", "Medium - Balanced performance and accuracy", "Large - Slow performance and high accuracy"], label="Model Type", value="Small - Better performance and less accuracy", info="Select the inference model before running predictions!") options_checkbox_img = gr.CheckboxGroup(["Show Boundary Box", "Show Segmentation Region", "Show Segmentation Boundary"], label="Options") conf_thres_img = gr.Slider(1, 100, value=60, label="Confidence Threshold", info="Choose the threshold above which objects should be detected") submit_btn_img = gr.Button(value="Predict") with gr.Column(scale=2): with gr.Row(): segmentation_img_output = gr.Image(height=300, label="Segmentation") depth_img_output = gr.Image(height=300, label="Depth Estimation") with gr.Row(): dist_img_output = gr.Image(height=300, label="Distance") pcd_img_output = gr.Plot(label="Point Cloud") gr.Markdown("## Sample Images") gr.Examples( examples=[os.path.join(os.path.dirname(__file__), "assets/images/baggage_claim.jpg"), os.path.join(os.path.dirname(__file__), "assets/images/kitchen_2.png"), os.path.join(os.path.dirname(__file__), "assets/images/soccer.jpg"), os.path.join(os.path.dirname(__file__), "assets/images/room_2.png"), os.path.join(os.path.dirname(__file__), "assets/images/living_room.jpg")], inputs=img_input, outputs=[segmentation_img_output, depth_img_output, dist_img_output, pcd_img_output], fn=process_image, cache_examples=True, ) with gr.Tab("Video"): with gr.Row(): with gr.Column(scale=1): vid_input = gr.Video() model_type_vid = gr.Dropdown( ["Small - Better performance and less accuracy", "Medium - Balanced performance and accuracy", "Large - Slow performance and high accuracy"], label="Model Type", value="Small - Better performance and less accuracy", info="Select the inference model before running predictions!") options_checkbox_vid = gr.CheckboxGroup(["Show Boundary Box", "Show Segmentation Region", "Show Segmentation Boundary"], label="Options") conf_thres_vid = gr.Slider(1, 100, value=60, label="Confidence Threshold", info="Choose the threshold above which objects should be detected") with gr.Row(): cancel_btn = gr.Button(value="Cancel") submit_btn_vid = gr.Button(value="Predict") with gr.Column(scale=2): with gr.Row(): segmentation_vid_output = gr.Image(height=300, label="Segmentation") depth_vid_output = gr.Image(height=300, label="Depth Estimation") with gr.Row(): dist_vid_output = gr.Image(height=300, label="Distance") gr.Markdown("## Sample Videos") gr.Examples( examples=[os.path.join(os.path.dirname(__file__), "assets/videos/input_video.mp4"), os.path.join(os.path.dirname(__file__), "assets/videos/driving.mp4"), os.path.join(os.path.dirname(__file__), "assets/videos/overpass.mp4"), os.path.join(os.path.dirname(__file__), "assets/videos/walking.mp4")], inputs=vid_input, # outputs=vid_output, # fn=vid_segmenation, ) # image tab logic submit_btn_img.click(process_image, inputs=img_input, outputs=[segmentation_img_output, depth_img_output, dist_img_output, pcd_img_output]) options_checkbox_img.change(update_segmentation_options, options_checkbox_img, []) conf_thres_img.change(update_confidence_threshold, conf_thres_img, []) model_type_img.change(model_selector, model_type_img, []) # video tab logic submit_btn_vid.click(process_video, inputs=vid_input, outputs=[segmentation_vid_output, depth_vid_output, dist_vid_output]) model_type_vid.change(model_selector, model_type_vid, []) cancel_btn.click(cancel, inputs=[], outputs=[]) options_checkbox_vid.change(update_segmentation_options, options_checkbox_vid, []) conf_thres_vid.change(update_confidence_threshold, conf_thres_vid, []) my_app.queue(concurrency_count=5, max_size=20).launch()