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2a2ae7e
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Parent(s):
754b1cc
Update app.py
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app.py
CHANGED
@@ -4,13 +4,8 @@ import cv2 # opencv2 package for python.
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import torch
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from pytube import YouTube
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from ultralyticsplus import YOLO, render_result
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#from imageai.Detection import ObjectDetection
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#obj_detect.setModelPath(r"C:/Datasets/yolo.h5")
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#obj_detect.loadModel()
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#from torch import hub # Hub contains other models like FasterRCNN
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model = YOLO('ultralyticsplus/yolov8s')
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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URL = "https://www.youtube.com/watch?v=dQw4w9WgXcQ" #URL to parse
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@@ -22,10 +17,6 @@ model.overrides['agnostic_nms'] = False # NMS class-agnostic
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model.overrides['max_det'] = 1000 # maximum number of detections per image
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model.to(device)
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#class Player:
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def load(URL):
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yt = YouTube(URL)
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@@ -36,19 +27,17 @@ def load(URL):
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frame_count = int(player.get(cv2.CAP_PROP_FRAME_COUNT))
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frame_fps = (player.get(cv2.CAP_PROP_FPS))
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#process.release()
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return vid_cap,frame_num,frame_count,frame_fps
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def vid_play(cap,frame_num):
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#player = cv2.VideoCapture(cap)
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assert player.isOpened() # Make sure that their is a stream.
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player.set(cv2.CAP_PROP_POS_FRAMES, int(frame_num))
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ret, frame_bgr = player.read(int(frame_num))
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frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
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results = model.predict(frame)
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render = render_result(model=model, image=frame, result=results[0])
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return render
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def fw_fn(cur,last):
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next = cur+1
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if next > last:
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@@ -65,25 +54,19 @@ def tog_off():
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return 0
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def pl_fn(cap,cur,last,fps,pl_tog):
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#player = cv2.VideoCapture(cap)
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#assert player.isOpened() # Make sure that their is a stream.
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player.set(cv2.CAP_PROP_POS_FRAMES, cur)
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ret, frame_bgr = player.read(cur)
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frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
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results = model.predict(frame)
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render = render_result(model=model, image=frame, result=results[0])
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if pl_tog ==1:
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#ins_cnt+=1
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cur+=1
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else:
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cur = cur
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return render,cur
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with gr.Blocks() as app:
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with gr.Row():
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with gr.Column():
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youtube_url = gr.Textbox(label="YouTube URL",value=f"{URL}")
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@@ -91,10 +74,10 @@ with gr.Blocks() as app:
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output_win = gr.Video()
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with gr.Column():
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with gr.Row():
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cur_frame = gr.Number()
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fps_frames = gr.Number()
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total_frames = gr.Number(interactive=False)
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run_button = gr.Button()
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with gr.Row():
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bk = gr.Button("<")
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pl = gr.Button("Play")
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@@ -102,17 +85,15 @@ with gr.Blocks() as app:
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fw = gr.Button(">")
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det_win = gr.Image(source="webcam", streaming=True)
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with gr.Row():
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pl_tog=gr.Number()
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ins_cnt=gr.Number()
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pl.click(tog_on,None,pl_tog)
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st.click(tog_off,None,pl_tog)
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pl_tog.change(pl_fn,[output_win,cur_frame,total_frames,fps_frames,pl_tog],[det_win,cur_frame],show_progress=False)
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#cur_frame.change(pl_fn,[output_win,cur_frame,total_frames,fps_frames,pl_tog],[det_win,cur_frame],every=0.1,show_progress=False)
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cur_frame.change(pl_fn,[output_win,cur_frame,total_frames,fps_frames,pl_tog],[det_win,cur_frame],show_progress=False)
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bk.click(bk_fn,[cur_frame],cur_frame)
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fw.click(fw_fn,[cur_frame,total_frames],cur_frame)
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load_button.click(load,youtube_url,[output_win,cur_frame,total_frames,fps_frames])
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run_button.click(vid_play, [output_win,cur_frame], det_win)
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app.queue(concurrency_count=10).launch()
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import torch
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from pytube import YouTube
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from ultralyticsplus import YOLO, render_result
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model = YOLO('ultralyticsplus/yolov8s')
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device = 'cuda' if torch.cuda.is_available() else 'cpu'
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URL = "https://www.youtube.com/watch?v=dQw4w9WgXcQ" #URL to parse
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model.overrides['max_det'] = 1000 # maximum number of detections per image
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model.to(device)
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def load(URL):
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yt = YouTube(URL)
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frame_count = int(player.get(cv2.CAP_PROP_FRAME_COUNT))
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frame_fps = (player.get(cv2.CAP_PROP_FPS))
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#process.release()
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return vid_cap,frame_num,frame_count,frame_fps
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def vid_play(cap,frame_num):
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assert player.isOpened() # Make sure that their is a stream.
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player.set(cv2.CAP_PROP_POS_FRAMES, int(frame_num))
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ret, frame_bgr = player.read(int(frame_num))
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frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
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results = model.predict(frame)
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render = render_result(model=model, image=frame, result=results[0])
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return render
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def fw_fn(cur,last):
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next = cur+1
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if next > last:
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return 0
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def pl_fn(cap,cur,last,fps,pl_tog):
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player.set(cv2.CAP_PROP_POS_FRAMES, cur)
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ret, frame_bgr = player.read(cur)
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frame = cv2.cvtColor(frame_bgr, cv2.COLOR_BGR2RGB)
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results = model.predict(frame)
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render = render_result(model=model, image=frame, result=results[0])
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if pl_tog ==1:
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cur+=1
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else:
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cur = cur
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return render,cur
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with gr.Blocks() as app:
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gr.Markdown("""<center><h1>Mediocre Video Object Detection</h1></center>""")
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with gr.Row():
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with gr.Column():
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youtube_url = gr.Textbox(label="YouTube URL",value=f"{URL}")
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output_win = gr.Video()
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with gr.Column():
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with gr.Row():
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cur_frame = gr.Number(label="Current Frame")
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fps_frames = gr.Number(label="Video FPS",interactive=False)
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total_frames = gr.Number(label="Total Frames",interactive=False)
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#run_button = gr.Button()
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with gr.Row():
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bk = gr.Button("<")
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pl = gr.Button("Play")
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fw = gr.Button(">")
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det_win = gr.Image(source="webcam", streaming=True)
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with gr.Row():
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pl_tog=gr.Number(visible=False)
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ins_cnt=gr.Number(visible=False)
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pl.click(tog_on,None,pl_tog,show_progress=False)
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st.click(tog_off,None,pl_tog,show_progress=False)
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pl_tog.change(pl_fn,[output_win,cur_frame,total_frames,fps_frames,pl_tog],[det_win,cur_frame],show_progress=False)
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cur_frame.change(pl_fn,[output_win,cur_frame,total_frames,fps_frames,pl_tog],[det_win,cur_frame],show_progress=False)
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bk.click(bk_fn,[cur_frame],cur_frame,show_progress=False)
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fw.click(fw_fn,[cur_frame,total_frames],cur_frame,show_progress=False)
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load_button.click(load,youtube_url,[output_win,cur_frame,total_frames,fps_frames])
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#run_button.click(vid_play, [output_win,cur_frame], det_win)
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app.queue(concurrency_count=10).launch()
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