Spaces:
Sleeping
Sleeping
| import gradio as gr | |
| #import torch | |
| import yolov5 | |
| 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, | |
| ): | |
| """ | |
| YOLOv5 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 = yolov5.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] | |
| demo_app = gr.Interface( | |
| fn=image_fn, | |
| inputs=[ | |
| gr.inputs.Image(type="pil", label="Input Image"), | |
| gr.inputs.Dropdown( | |
| choices=[ | |
| "alshimaa/yolo5_epoch100", | |
| #"kadirnar/yolov7-v0.1", | |
| ], | |
| default="alshimaa/yolo5_epoch100", | |
| 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="Object Detector: Identify People Without Mask", | |
| examples=[['img1.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img2.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45], ['img3.png', 'alshimaa/yolo5_epoch100', 640, 0.25, 0.45]], | |
| cache_examples=True, | |
| live=True, | |
| theme='huggingface', | |
| ) | |
| demo_app.launch(debug=True, enable_queue=True) | |
| # 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, | |
| # ): | |
| # """ | |
| # YOLOv5 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 = yolov5.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 = yolov5.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/SEE_model_yolo7", | |
| # #"kadirnar/yolov7-v0.1", | |
| # ], | |
| # default="alshimaa/SEE_model_yolo7", | |
| # 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/SEE_model_yolo7', 640, 0.25, 0.45], ['image2.jpg', 'alshimaa/SEE_model_yolo7', 640, 0.25, 0.45], ['image3.jpg', 'alshimaa/SEE_model_yolo7', 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", | |
| # #"kadirnar/yolov7-v0.1", | |
| # ], | |
| # 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() | |