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| import gradio as gr | |
| import pandas as pd | |
| import random | |
| import os | |
| import torch | |
| import torch.nn.functional as F | |
| from mobilenet import MobileNetLarge3D, MobileNetSmall3D | |
| from torchvision.io import read_video | |
| import time | |
| def classify_pitch(confidence_scores): | |
| if torch.argmax(confidence_scores) == 0: | |
| call = 'Ball' | |
| elif torch.argmax(confidence_scores) == 1: | |
| call = 'Strike' | |
| else: | |
| print("that's odd, something is wrong") | |
| pass | |
| return call | |
| def get_demo_call(video_name): | |
| video_name = os.path.basename(video_name).lower() | |
| if video_name.startswith("strike"): | |
| ump_out = "Strike" | |
| elif video_name.startswith("ball"): | |
| ump_out = "Ball" | |
| else: | |
| ump_out = None | |
| return ump_out | |
| def call_pitch(pitch): | |
| start = time.time() | |
| std = (0.2104, 0.1986, 0.1829) | |
| mean = (0.3939, 0.3817, 0.3314) | |
| # Convert the mean and std to tensors | |
| mean = torch.tensor(mean).view(1, 3, 1, 1, 1) | |
| std = torch.tensor(std).view(1, 3, 1, 1, 1) | |
| ump_out = get_demo_call(pitch) | |
| pitch_tensor = (read_video(pitch,pts_unit='sec')[0].permute(-1,0,1,2)).unsqueeze(0)/255 | |
| pitch_tensor = (pitch_tensor-mean)/std #normalize the pitch tensor | |
| video_length = pitch_tensor.shape[2] #get video length to wait for the video to finish | |
| model = MobileNetSmall3D() | |
| model.load_state_dict(torch.load('weights/MobileNetSmall.pth',map_location=torch.device('cpu'))) | |
| model.eval() | |
| #run the model | |
| with torch.no_grad(): | |
| output = model(pitch_tensor) | |
| output = F.softmax(output,dim=1) | |
| final_call = classify_pitch(output) | |
| # time.sleep(video_length-1.5) #wait until the video is done to return the call | |
| while time.time() - start < (video_length-5)/15: #wait until the video is done to return the call and go 2 frames early to seem speedy | |
| time.sleep(0.1) | |
| if ump_out is not None: | |
| return final_call,ump_out | |
| else: | |
| return final_call | |
| def generate_random_pitch(): | |
| random_number = random.randint(1,2645342) #random number in our range to select a random pitch | |
| df = pd.read_csv('picklebot_2m.csv',skiprows=random_number,nrows=1,header=None) #just load one row | |
| video_link = df.values[0][0] | |
| label = df.values[0][1] | |
| if label == 0: | |
| ump_out = "Ball" | |
| elif label == 1: | |
| ump_out = "Strike" | |
| else: | |
| #error if it's not a 0 or 1 | |
| ump_out = "Error" | |
| random_pitch = download_pitch(video_link,ump_out) | |
| return random_pitch | |
| def download_pitch(video_link,ump_out=None): | |
| #download and process the video link using yt-dlp and ffmpeg | |
| if ump_out is not None: | |
| video_out = f"demo_files/downloaded_videos/{ump_out}_demo_video.mp4" | |
| os.system(f"yt-dlp -q --no-warnings --force-overwrites -f mp4 {video_link} -o '{video_out}'") | |
| os.system(f'ffmpeg -y -nostats -loglevel 0 -i {video_out} -vf "crop=700:700:in_w/2-350:in_h/2-350,scale=224:224" -r 15 -c:v libx264 -crf 23 -c:a aac -an -strict experimental {video_out}_cropped_video.mp4') | |
| else: | |
| video_out = f"demo_files/downloaded_videos/own_demo_video.mp4" | |
| os.system(f"yt-dlp -q --no-warnings --force-overwrites -f mp4 {video_link} -o '{video_out}'") | |
| os.system(f'ffmpeg -y -nostats -loglevel 0 -i {video_out} -vf "crop=700:700:in_w/2-350:in_h/2-350,scale=224:224" -r 15 -c:v libx264 -crf 23 -c:a aac -strict experimental {video_out}_cropped_video.mp4') | |
| return f"{video_out}_cropped_video.mp4" | |
| demo_files = os.listdir("demo_files/") | |
| demo_files = [os.path.join("demo_files/", file) for file in demo_files if file.endswith(".mp4")] | |
| with gr.Blocks(title="Picklebot") as demo: | |
| with gr.Tab("Picklebot"): | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| inp = gr.Video(interactive=False, label="Pitch Video") | |
| with gr.Column(scale=2): | |
| ump_out = gr.Label(label="Umpire's Original Call") | |
| pb_out = gr.Label(label="Picklebot's Call") | |
| random_button = gr.Button("🔀 Load a Random Pitch") | |
| random_button.click(fn=generate_random_pitch,outputs=[inp]) | |
| inp.play(fn=call_pitch,inputs=inp,outputs=[pb_out,ump_out]) | |
| gr.ClearButton([inp,pb_out,ump_out]) | |
| gr.Examples(demo_files,inputs=inp,outputs=[inp]) | |
| with gr.Tab("Use Your Own Video!"): | |
| with gr.Row(): | |
| gr.Markdown(value= | |
| """ | |
| # Here\'s how to use your own video: | |
| 1. Navigate to [Baseball Savant's statcast search](https://baseballsavant.mlb.com/statcast_search) | |
| 2. Choose the situation and pitch you want (just keep in mind that the network was only trained on called balls and called strikes) | |
| 3. Click on the pitcher or batter's name to get to list of pitches | |
| 4. Right click on the camera icon to the right, and copy the link | |
| 5. Paste the video url in below, and press go to download the pitch | |
| 6. Watch the video and see what Picklebot thinks! | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=3): | |
| vid_inp = gr.Video(interactive=False, label="Pitch Video") | |
| with gr.Column(scale=2): | |
| #make a textbox to take in the user's video link | |
| input_txt = gr.Textbox(placeholder="Paste your video link here",label="Video Link") | |
| input_txt.submit(fn=download_pitch,inputs=input_txt,outputs=vid_inp) | |
| submit_button = gr.Button("Go!") | |
| submit_button.click(fn=download_pitch,inputs=input_txt,outputs=vid_inp) | |
| pb_out = gr.Label(label="Picklebot's Call") | |
| vid_inp.play(fn=call_pitch,inputs=vid_inp,outputs=pb_out) | |
| gr.ClearButton([vid_inp,pb_out,input_txt]) | |
| with gr.Tab("About"): | |
| gr.Markdown(value= | |
| """Picklebot is a 3D Convolutional Neural Network that classifies baseball pitches as balls or strikes. | |
| It was trained on the [Picklebot-50K Dataset](https://huggingface.co/datasets/hbfreed/Picklebot-50K), | |
| comprised of over fifty thousand pitches to achieve ~80% accuracy.""") | |
| if __name__ == "__main__": | |
| demo.launch() |