app.py

from ultralytics import YOLO
import cv2
import gradio as gr
import numpy as np
import os
import torch

from image_segmenter import ImageSegmenter
from monocular_depth_estimator import MonocularDepthEstimator

# params
CANCEL_PROCESSING = False

img_seg = ImageSegmenter(model_type='n')
depth_estimator = MonocularDepthEstimator(side_by_side=False)

def process_image(image):
    return img_seg.predict(image), depth_estimator.make_prediction(image)

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 ....")
            yield cv2.cvtColor(img_seg.predict(frame), cv2.COLOR_BGR2RGB), depth_estimator.make_prediction(frame)
    
    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 cancel():
    CANCEL_PROCESSING = True

if __name__ == "__main__":
    # img_1 = cv2.imread("assets/images/bus.jpg")
    # pred_img = image_segmentation(img_1)
    # cv2.imshow("output", pred_img)
    # cv2.waitKey(0)
    # cv2.destroyAllWindows()

    # gradio gui app
    with gr.Blocks() as my_app:

        # title
        gr.Markdown(
        """
        # Object segmentation and depth estimation
        Input an image or Video
        """)

        # tabs
        with gr.Tab("Image"):
            with gr.Row():
                with gr.Column(scale=1):
                    img_input = gr.Image()
                    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")
            
            gr.Markdown("## Sample Images")
            gr.Examples(
                examples=[os.path.join(os.path.dirname(__file__), "assets/images/bus.jpg")],
                inputs=img_input,
                outputs=[segmentation_img_output, depth_img_output],
                fn=process_image,
                cache_examples=True,
            )

        with gr.Tab("Video"):
            with gr.Row():
                with gr.Column(scale=1):
                    vid_input = gr.Video()
                    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=400, label="Segmentation")
                        depth_vid_output = gr.Image(height=400, label="Depth Estimation")
            
            gr.Markdown("## Sample Videos")
            gr.Examples(
                examples=[os.path.join(os.path.dirname(__file__), "assets/videos/input_video.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])
        options_checkbox_img.change(update_segmentation_options, options_checkbox_img, [])
        conf_thres_img.change(update_confidence_threshold, conf_thres_img, [])

        # video tab logic
        submit_btn_vid.click(process_video, inputs=vid_input, outputs=[segmentation_vid_output, depth_vid_output])
        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()