app.py

try:
    import detectron2
except:
    import os
    os.system('pip install git+https://github.com/facebookresearch/detectron2.git')

import cv2
import torch
from matplotlib.pyplot import axis
import gradio as gr
import requests
import numpy as np
from torch import nn
import requests

import torch

from detectron2 import model_zoo
from detectron2.engine import DefaultPredictor
from detectron2.config import get_cfg
from detectron2.utils.visualizer import Visualizer
from detectron2.data import MetadataCatalog
from detectron2.utils.visualizer import ColorMode


model_path = "https://huggingface.co/asalhi85/Smartathon-Detectron2/resolve/9f4d573340b033e651d4937906f23850f9b6bc57/phase2_detectron_model.pth"

cfg = get_cfg()
cfg.merge_from_file("./faster_rcnn_X_101_32x8d_FPN_3x.yaml")
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 11
cfg.MODEL.WEIGHTS = model_path

my_metadata = MetadataCatalog.get("dbmdz_coco_all")
#my_metadata.thing_classes = ["GRAFFITI", "FADED_SIGNAGE","POTHOLES","GARBAGE","CONSTRUCTION_ROAD","BROKEN_SIGNAGE","BAD_STREETLIGHT","BAD_BILLBOARD","SAND_ON_ROAD","CLUTTER_SIDEWALK","UNKEPT_FACADE"]
my_metadata.thing_classes = ["None", "BAD_BILLBOARD","BROKEN_SIGNAGE","CLUTTER_SIDEWALK","CONSTRUCTION_ROAD","FADED_SIGNAGE","GARBAGE","GRAFFITI","POTHOLES","SAND_ON_ROAD","UNKEPT_FACADE"]

# #smart_dict={'GRAFFITI' : 0.0 , 'FADED_SIGNAGE': 1.0 , 'POTHOLES': 2.0,
#             'GARBAGE' : 3.0 , 'CONSTRUCTION_ROAD': 4.0 , 'BROKEN_SIGNAGE': 5.0,
#             'BAD_STREETLIGHT' : 6.0 , 'BAD_BILLBOARD': 7.0 , 'SAND_ON_ROAD':8.0,
#             'CLUTTER_SIDEWALK' : 9.0 , 'UNKEPT_FACADE': 10.0}
if not torch.cuda.is_available():
    cfg.MODEL.DEVICE = "cpu"


def inference(image_url, image, min_score):
    if image_url:
        r = requests.get(image_url)
        if r:
            im = np.frombuffer(r.content, dtype="uint8")
            im = cv2.imdecode(im, cv2.IMREAD_COLOR)
    else:
        im = cv2.imread(image)
        # Model expect BGR!
        #im = image[:,:,::-1]

    cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = min_score
    predictor = DefaultPredictor(cfg)

    outputs = predictor(im)

    v = Visualizer(im[:,:,::-1], my_metadata, scale=1.2, instance_mode=ColorMode.IMAGE )
    out = v.draw_instance_predictions(outputs["instances"].to("cpu"))

    return out.get_image()


title = "Smartathon Phase2 Demo - Baseer"
description = "This demo introduces an interactive playground for our trained Detectron2 model."
article = '<p>Detectron model is available from our repository <a href="https://github.com/asalhi/Smartathon-Baseer">here</a>.</p>'

# gr.Interface(
#     inference,
#     [gr.inputs.Textbox(label="Image URL", placeholder=""),
#      gr.inputs.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image"),
#      gr.Slider(minimum=0.0, maximum=1.0, value=0.4, label="Minimum score"),
#     ],
#     gr.outputs.Image(type="pil", label="Output"),
#     #gr.Examples(['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg'], inputs=gr.inputs.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image")),
#     title=title,
#     description=description,
#     article=article,
#     #examples=[['./d1.jpeg'], ['./d2.jpeg'], ['./d3.jpeg'],['./d4.jpeg'],['./d5.jpeg'],['./d6.jpeg']],
#     examples = gr.Examples(['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg'], inputs=gr.inputs.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image")),
#     cache_examples=False).launch()
#     #examples=['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg']


with gr.Blocks(title=title,
                    css=".gradio-container {background:white;}"
               ) as demo:

    gr.HTML("""<h4 style="font-weight:bold; text-align:center; color:navy;">"Smartathon Phase2 Demo - Baseer"</h4>""")
    # #
    #gr.HTML("""<h5 style="color:navy;">1- Select an example by clicking a thumbnail below.</h5>""")
    gr.HTML("""<h5 style="color:navy;">1- Select an example by clicking a thumbnail below.<br>
                                       2- Or upload an image by clicking on the canvas.<br>
                                       3- Or insert direct url of an image.</h5>""")

    with gr.Row():
        with gr.Column():
            #gr.HTML("""<h5 style="color:navy;">3- Or insert direct url of an image.</h5>""")
            input_url = gr.Textbox(label="Image URL", placeholder="")
            #gr.HTML("""<h5 style="color:navy;">2- Or upload an image by clicking on the canvas.<br></h5>""")
            input_image = gr.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image")
            gr.HTML("""<h5 style="color:navy;">4- You can use this slider to control boxes min score: </h5>""")
            sliderr = gr.Slider(minimum=0.0, maximum=1.0, value=0.4, label="Minimum score")
        output_image = gr.Image(type="pil", label="Output")

    # gr.Interface(
    #     inference,
    #     [gr.inputs.Textbox(label="Image URL", placeholder=""),
    #      gr.inputs.Image(type="filepath", image_mode="RGB", source="upload", optional=False, label="Input Image"),
    #      gr.Slider(minimum=0.0, maximum=1.0, value=0.4, label="Minimum score"),
    #     ],


    gr.Examples(['./d1.jpeg', './d2.jpeg', './d3.jpeg','./d4.jpeg','./d5.jpeg','./d6.jpeg'], inputs=input_image)

    #gr.HTML("""<br/>""")





    gr.HTML("""<h5 style="color:navy;">5- Then, click "Submit" button to predict object instances. It will take about 15-20 seconds (on cpu)</h5>""")
    send_btn = gr.Button("Submit")
    send_btn.click(fn=inference, inputs=[input_url,input_image,sliderr], outputs=[output_image], api_name="find")

    #gr.HTML("""<h5 style="color:navy;">Reference</h5>""")
    #gr.HTML("""<ul>""")
    gr.HTML("""<h5 style="color:navy;">Detectron model is available from our repository <a href="https://github.com/asalhi/Smartathon-Baseer">here</a>.</h5>""")
    #gr.HTML("""</ul>""")


#demo.queue()
demo.launch() # debug=True)