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# import gradio as gr
# #import torch
# import yolov7
# #
# # from huggingface_hub import hf_hub_download
# from huggingface_hub import HfApi
# # Images
# #torch.hub.download_url_to_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 yolov7_inference(
# image: gr.inputs.Image = None,
# model_path: gr.inputs.Dropdown = None,
# image_size: gr.inputs.Slider = 640,
# conf_threshold: gr.inputs.Slider = 0.25,
# iou_threshold: gr.inputs.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]
# inputs = [
# gr.inputs.Image(type="pil", label="Input Image"),
# gr.inputs.Dropdown(
# choices=[
# "alshimaa/model_baseline",
# "alshimaa/model_yolo7",
# #"kadirnar/yolov7-v0.1",
# ],
# default="alshimaa/model_baseline",
# 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.outputs.Image(type="filepath", label="Output Image")
# title = "Smart Environmental Eye (SEE)"
# examples = [['image1.jpg', 'alshimaa/model_yolo7', 640, 0.25, 0.45], ['image2.jpg', 'alshimaa/model_yolo7', 640, 0.25, 0.45], ['image3.jpg', 'alshimaa/model_yolo7', 640, 0.25, 0.45]]
# demo_app = gr.Interface(
# fn=yolov7_inference,
# inputs=inputs,
# outputs=outputs,
# title=title,
# examples=examples,
# cache_examples=True,
# theme='huggingface',
# )
# demo_app.launch(debug=True, enable_queue=True)
import subprocess
import tempfile
import time
from pathlib import Path
import cv2
import gradio as gr
from inferer import Inferer
pipeline = Inferer("alshimaa/model_baseline", device='cuda')
def fn_image(image, conf_thres, iou_thres):
return pipeline(image, conf_thres, iou_thres)
def fn_video(video_file, conf_thres, iou_thres, start_sec, duration):
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(pipeline(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=fn_image,
inputs=[
"image",
gr.Slider(0, 1, value=0.5, label="Confidence Threshold"),
gr.Slider(0, 1, value=0.5, label="IOU Threshold"),
],
outputs=gr.Image(type="file"),
examples=[["image1.jpg", 0.5, 0.5], ["image2.jpg", 0.25, 0.45], ["image3.jpg", 0.25, 0.45]],
title="Smart Environmental Eye (SEE)",
allow_flagging=False,
allow_screenshot=False,
)
video_interface = gr.Interface(
fn=fn_video,
inputs=[
gr.Video(type="file"),
gr.Slider(0, 1, value=0.25, label="Confidence Threshold"),
gr.Slider(0, 1, value=0.45, label="IOU Threshold"),
gr.Slider(0, 10, value=0, label="Start Second", step=1),
gr.Slider(0, 10 if pipeline.device.type != 'cpu' else 3, value=4, label="Duration", step=1),
],
outputs=gr.Video(type="file", format="mp4"),
# examples=[
# ["video.mp4", 0.25, 0.45, 0, 2],
# ],
title="Smart Environmental Eye (SEE)",
allow_flagging=False,
allow_screenshot=False,
)
if __name__ == "__main__":
gr.TabbedInterface(
[image_interface, video_interface],
["Run on Images", "Run on Videos"],
).launch()
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