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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_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 image_fn(
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]
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.inputs.Image(type="pil", label="Input Image"),
gr.inputs.Dropdown(
choices=[
"alshimaa/yolov7",
#"kadirnar/yolov7-v0.1",
],
default="alshimaa/yolov7",
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/yolov7', 640, 0.25, 0.45], ['image2.jpg', 'alshimaa/yolov7', 640, 0.25, 0.45], ['image3.jpg', 'alshimaa/yolov7', 640, 0.25, 0.45]],
cache_examples=True,
theme='huggingface',
)
video_interface = gr.Interface(
fn=video_fn,
inputs=[
gr.inputs.Video(source = "upload", type = "mp4", label = "Input Video"),
gr.inputs.Dropdown(
choices=[
"alshimaa/SEE_model_yolo7"
],
default="alshimaa/SEE_model_yolo7",
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()
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