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()