app.py

import gradio as gr
#import torch
import yolov7
import subprocess
import tempfile
import time
from pathlib import Path
import uuid
import cv2
import gradio as gr



# # Images
# #torch.hub.download_url_t
# o_file('https://github.com/ultralytics/yolov5/raw/master/data/images/zidane.jpg', 'zidane.jpg')
# #torch.hub.download_url_to_file('https://raw.githubusercontent.com/obss/sahi/main/tests/data/small-vehicles1.jpeg', 'small-vehicles1.jpeg')

    
def image_fn(
    image: gr.Image = None,
    model_path: gr.Dropdown = None,
    image_size: gr.Slider = 640,
    conf_threshold: gr.Slider = 0.25,
    iou_threshold: gr.Slider = 0.45,
):
    """
    YOLOv7 inference function
    Args:
        image: Input image
        model_path: Path to the model
        image_size: Image size
        conf_threshold: Confidence threshold
        iou_threshold: IOU threshold
    Returns:
        Rendered image
    """

    model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
    model.conf = conf_threshold
    model.iou = iou_threshold
    results = model([image], size=image_size)
    return results.render()[0]
  
  
        
def video_fn(model_path, video_file, conf_thres, iou_thres, start_sec, duration):
    model = yolov7.load(model_path, device="cpu", hf_model=True, trace=False)
    start_timestamp = time.strftime("%H:%M:%S", time.gmtime(start_sec))
    end_timestamp = time.strftime("%H:%M:%S", time.gmtime(start_sec + duration))

    suffix = Path(video_file).suffix

    clip_temp_file = tempfile.NamedTemporaryFile(suffix=suffix)
    subprocess.call(
        f"ffmpeg -y -ss {start_timestamp} -i {video_file} -to {end_timestamp} -c copy {clip_temp_file.name}".split()
    )

    # Reader of clip file
    cap = cv2.VideoCapture(clip_temp_file.name)

    # This is an intermediary temp file where we'll write the video to
    # Unfortunately, gradio doesn't play too nice with videos rn so we have to do some hackiness
    # with ffmpeg at the end of the function here.
    with tempfile.NamedTemporaryFile(suffix=".mp4") as temp_file:
        out = cv2.VideoWriter(temp_file.name, cv2.VideoWriter_fourcc(*"MP4V"), 30, (1280, 720))

        num_frames = 0
        max_frames = duration * 30
        while cap.isOpened():
            try:
                ret, frame = cap.read()
                if not ret:
                    break
            except Exception as e:
                print(e)
                continue
            print("FRAME DTYPE", type(frame))
            out.write(model([frame], conf_thres, iou_thres))
            num_frames += 1
            print("Processed {} frames".format(num_frames))
            if num_frames == max_frames:
                break

        out.release()

        # Aforementioned hackiness
        out_file = tempfile.NamedTemporaryFile(suffix="out.mp4", delete=False)
        subprocess.run(f"ffmpeg -y -loglevel quiet -stats -i {temp_file.name} -c:v libx264 {out_file.name}".split())

    return out_file.name

image_interface = gr.Interface(
    fn=image_fn,
    inputs=[
    gr.Image(type="pil", label="Input Image"),
    gr.Dropdown(
        choices=[
            "Aalaa/Yolov7_Visual_Pollution_Detection",
            #"kadirnar/yolov7-v0.1",
        ],
        label="Model",
    )
    #gr.inputs.Slider(minimum=320, maximum=1280, default=640, step=32, label="Image Size")
    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.25, step=0.05, label="Confidence Threshold"),
    #gr.inputs.Slider(minimum=0.0, maximum=1.0, default=0.45, step=0.05, label="IOU Threshold")
],
    outputs=gr.Image(type="filepath", label="Output Image"),
    title="Smart Environmental Eye (SEE)",
    examples=[['image1.jpg', 'Aalaa/Yolov7_Visual_Pollution_Detection', 640, 0.25, 0.45]],
    cache_examples=True,
    theme='huggingface',
)


video_interface = gr.Interface(
    fn=video_fn,
    inputs=[
        gr.Video(source = "upload", type = "mp4", label = "Input Video"),
        gr.Dropdown(
        choices=[
            "Aalaa/Yolov7_Visual_Pollution_Detection",
            #"kadirnar/yolov7-v0.1",
        ],
        label="Model",
    ),
    ],
    outputs=gr.outputs.Video(type = "mp4", label = "Output Video"),
    # examples=[
    #     ["video.mp4", 0.25, 0.45, 0, 2],
       
    # ],
    title="Smart Environmental Eye (SEE)",
    cache_examples=True,
    theme='huggingface',
   
)

if __name__ == "__main__":
    gr.TabbedInterface(
        [image_interface, video_interface],
        ["Run on Images", "Run on Videos"],
    ).launch()