app.py

import gradio as gr
import numpy as np
from PIL import Image
import os
import cv2
import math
import spaces
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import concurrent.futures
from scipy.signal import medfilt, find_peaks
from functools import partial
from passlib.hash import pbkdf2_sha256
from tqdm import tqdm
import pandas as pd
import plotly.express as px
import onnxruntime as ort
import torch
from torchvision import transforms
import torchvision.transforms.functional as F

from huggingface_hub import hf_hub_download
from huggingface_hub import HfApi

from hls_download import download_clips

plt.style.use('dark_background')

IMG_SIZE = 256

onnx_file = hf_hub_download(repo_id="dylanplummer/ropenet", filename="nextjump.onnx", repo_type="model", token=os.environ['DATASET_SECRET'])
# model_xml = hf_hub_download(repo_id="dylanplummer/ropenet", filename="model.xml", repo_type="model", token=os.environ['DATASET_SECRET'])
# hf_hub_download(repo_id="dylanplummer/ropenet", filename="model.mapping", repo_type="model", token=os.environ['DATASET_SECRET'])
#model_xml = "model_ir/model.xml"

# ie = Core()
# model_ir = ie.read_model(model=model_xml)
# config = {"PERFORMANCE_HINT": "LATENCY"}
# compiled_model_ir = ie.compile_model(model=model_ir, device_name="CPU", config=config)

if torch.cuda.is_available():
    providers = [("CUDAExecutionProvider", {"device_id": torch.cuda.current_device(),
                                            "user_compute_stream": str(torch.cuda.current_stream().cuda_stream)})]
    sess_options = ort.SessionOptions()
    ort_sess = ort.InferenceSession(onnx_file, sess_options=sess_options, providers=providers)
else:
    ort_sess = ort.InferenceSession(onnx_file)

# warmup inference
ort_sess.run(None, {'video': np.zeros((4, 64, 3, IMG_SIZE, IMG_SIZE), dtype=np.float32)})


class SquarePad:
    # https://discuss.pytorch.org/t/how-to-resize-and-pad-in-a-torchvision-transforms-compose/71850/9
	def __call__(self, image):
		w, h = image.size
		max_wh = max(w, h)
		hp = int((max_wh - w) / 2)
		vp = int((max_wh - h) / 2)
		padding = (hp, vp, hp, vp)
		return F.pad(image, padding, 0, 'constant')
    
def square_pad_opencv(image):
    h, w = image.shape[:2]
    max_wh = max(w, h)
    hp = int((max_wh - w) / 2)
    vp = int((max_wh - h) / 2)
    return cv2.copyMakeBorder(image, vp, vp, hp, hp, cv2.BORDER_CONSTANT, value=[0, 0, 0])

def sigmoid(x):
    return 1 / (1 + np.exp(-x))


def preprocess_image(img, img_size):
    #img = square_pad_opencv(img)
    #img = cv2.resize(img, (img_size, img_size), interpolation=cv2.INTER_CUBIC)
    img = Image.fromarray(img)
    transforms_list = []
    transforms_list.append(transforms.ToTensor())
    preprocess = transforms.Compose(transforms_list)
    return preprocess(img).unsqueeze(0)

def run_inference(batch_X):
    batch_X = torch.cat(batch_X)
    return ort_sess.run(None, {'video': batch_X.numpy()})


@spaces.GPU()
def inference(x, count_only_api, api_key, 
              img_size=IMG_SIZE, seq_len=64, stride_length=32, stride_pad=3, batch_size=4, 
              miss_threshold=0.8, marks_threshold=0.5, median_pred_filter=True, center_crop=True, both_feet=True, 
              api_call=False,
              progress=gr.Progress()):
    progress(0, desc="Starting...")
    #x = download_clips(stream_url, os.getcwd(), start_time, end_time)
    # check if GPU is available
    #api = HfApi(token=os.environ['DATASET_SECRET'])
    #out_file = str(uuid.uuid1())
    has_access = False
    if api_call:
        has_access = pbkdf2_sha256.verify(os.environ['DEV_API_TOKEN'], api_key)
        if not has_access:
            return "Invalid API Key"
        
    cap = cv2.VideoCapture(x)
    length = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    frame_height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
    frame_width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
    period_length_overlaps = np.zeros(length + seq_len)
    fps = int(cap.get(cv2.CAP_PROP_FPS))
    seconds = length / fps
    all_frames = []
    frame_i = 1
    resize_size = max(frame_width, frame_height)
    while cap.isOpened():
        ret, frame = cap.read()
        if ret is False:
            frame = all_frames[-1]  # padding will be with last frame
            break
        frame = cv2.cvtColor(np.uint8(frame), cv2.COLOR_BGR2RGB)
        frame = cv2.resize(frame, (resize_size, resize_size), interpolation=cv2.INTER_CUBIC)
        frame_center_x = frame.shape[1] // 2
        frame_center_y = frame.shape[0] // 2
        crop_x = frame_center_x - img_size // 2
        crop_y = frame_center_y - img_size // 2
        frame = frame[crop_y:crop_y+img_size, crop_x:crop_x+img_size]
        all_frames.append(frame)
        frame_i += 1
    cap.release()

    length = len(all_frames)
    period_lengths = np.zeros(len(all_frames) + seq_len + stride_length)
    periodicities = np.zeros(len(all_frames) + seq_len + stride_length)
    full_marks = np.zeros(len(all_frames) + seq_len + stride_length)
    event_type_logits = np.zeros((len(all_frames) + seq_len + stride_length, 7))
    period_length_overlaps = np.zeros(len(all_frames) + seq_len + stride_length)
    event_type_logit_overlaps = np.zeros((len(all_frames) + seq_len + stride_length, 7))
    for _ in range(seq_len + stride_length):  # pad full sequence
        all_frames.append(all_frames[-1])
    batch_list = []
    idx_list = []
    inference_futures = []
    with concurrent.futures.ThreadPoolExecutor(max_workers=4) as executor:
        for i in tqdm(range(0, length + stride_length - stride_pad, stride_length)):
            batch = all_frames[i:i + seq_len]
            Xlist = []
            preprocess_tasks = [(idx, executor.submit(preprocess_image, img, img_size)) for idx, img in enumerate(batch)]
            for idx, future in sorted(preprocess_tasks, key=lambda x: x[0]):
                Xlist.append(future.result())
            
            if len(Xlist) < seq_len:
                for _ in range(seq_len - len(Xlist)):
                    Xlist.append(Xlist[-1])
            
            X = torch.cat(Xlist)
            X *= 255
            batch_list.append(X.unsqueeze(0))
            idx_list.append(i)
            
            if len(batch_list) == batch_size:
                future = executor.submit(run_inference, batch_list)
                inference_futures.append((batch_list, idx_list, future))
                batch_list = []
                idx_list = []
        
        # Process any remaining batches
        if batch_list:
            while len(batch_list) != batch_size:
                batch_list.append(batch_list[-1])
                idx_list.append(idx_list[-1])
            future = executor.submit(run_inference, batch_list)
            inference_futures.append((batch_list, idx_list, future))
        
        # Collect and process the inference results
        for batch_list, idx_list, future in inference_futures:
            outputs = future.result()
            y1_out = outputs[0]
            y2_out = outputs[1]
            y3_out = outputs[2]
            y4_out = outputs[3]
            for y1, y2, y3, y4, idx in zip(y1_out, y2_out, y3_out, y4_out, idx_list):
                periodLength = y1.squeeze()
                periodicity = y2.squeeze()
                marks = y3.squeeze()
                event_type = y4.squeeze()
                period_lengths[idx:idx+seq_len] += periodLength
                periodicities[idx:idx+seq_len] += periodicity
                full_marks[idx:idx+seq_len] += marks
                event_type_logits[idx:idx+seq_len] += event_type
                period_length_overlaps[idx:idx+seq_len] += 1
                event_type_logit_overlaps[idx:idx+seq_len] += 1
            
    periodLength = np.divide(period_lengths, period_length_overlaps, where=period_length_overlaps!=0)[:length]
    periodicity = np.divide(periodicities, period_length_overlaps, where=period_length_overlaps!=0)[:length]
    full_marks = np.divide(full_marks, period_length_overlaps, where=period_length_overlaps!=0)[:length]
    per_frame_event_type_logits = np.divide(event_type_logits, event_type_logit_overlaps, where=event_type_logit_overlaps!=0)[:length]
    event_type_logits = np.mean(per_frame_event_type_logits, axis=0)
    # softmax of event type logits  
    event_type_probs = np.exp(event_type_logits) / np.sum(np.exp(event_type_logits))
    per_frame_event_types = np.argmax(per_frame_event_type_logits, axis=1)
    
    if median_pred_filter:
        periodicity = medfilt(periodicity, 5)
        periodLength = medfilt(periodLength, 5)
    periodicity = sigmoid(periodicity)
    full_marks = sigmoid(full_marks)
    pred_marks_peaks, _ = find_peaks(full_marks, distance=3, height=marks_threshold)
    full_marks_mask = np.zeros(len(full_marks))
    full_marks_mask[pred_marks_peaks] = 1
    periodicity_mask = np.int32(periodicity > miss_threshold)
    numofReps = 0
    count = []
    for i in range(len(periodLength)):
        if periodLength[i] < 2 or periodicity_mask[i] == 0:
            numofReps += 0
        elif full_marks_mask[i]:  # high confidence mark detected
            if math.modf(numofReps)[0] < 0.2:  # probably false positive/late detection
                numofReps = float(int(numofReps))
            else:
                numofReps = float(int(numofReps) + 1.01)  # round up
        else:
            numofReps += max(0, periodicity_mask[i]/(periodLength[i]))
        count.append(round(float(numofReps), 2))
    count_pred = count[-1]
    marks_count_pred = 0
    for i in range(len(full_marks) - 1):
        # if a jump was counted, and periodicity is high, and the next frame was not counted (to avoid double counting)
        if full_marks_mask[i] > 0 and periodicity_mask[i] > 0 and full_marks_mask[i + 1] == 0:
            marks_count_pred += 1
    if not both_feet:
        count_pred = count_pred / 2
        marks_count_pred = marks_count_pred / 2
        count = np.array(count) / 2
    try:
        confidence = (np.mean(periodicity[periodicity > miss_threshold]) - miss_threshold) / (1 - miss_threshold)
    except ZeroDivisionError:
        confidence = 0
    self_err = abs(count_pred - marks_count_pred)
    try:
        self_pct_err = self_err / count_pred
    except ZeroDivisionError:
        self_pct_err = 0
    total_confidence = confidence * (1 - self_pct_err)

    if both_feet:
        count_msg = f"## Reps Count (both feet): {count_pred:.1f}, Confidence: {total_confidence:.2f}"
    else:
        count_msg = f"## Predicted Count (one foot): {count_pred:.1f}, Confidence: {total_confidence:.2f}"

    if api_call:
        if count_only_api:
            return f"{count_pred:.2f} (conf: {total_confidence:.2f})"
        else:
            return np.array2string(periodLength, formatter={'float_kind':lambda x: "%.2f" % x}).replace('\n', ''), \
                np.array2string(periodicity, formatter={'float_kind':lambda x: "%.2f" % x}).replace('\n', ''), \
                np.array2string(full_marks, formatter={'float_kind':lambda x: "%.2f" % x}).replace('\n', ''), \
                f"reps: {count_pred:.2f}, marks: {marks_count_pred:.1f}, confidence: {total_confidence:.2f}", \
                f"single_rope_speed: {event_type_probs[0]:.3f}, double_dutch: {event_type_probs[1]:.3f}, double_unders: {event_type_probs[2]:.3f}, single_bounce: {event_type_probs[3]:.3f}"
   

    jumps_per_second = np.clip(1 / ((periodLength / fps) + 0.01), 0, 10)
    jumping_speed = np.copy(jumps_per_second)
    misses = periodicity < miss_threshold
    jumps_per_second[misses] = 0
    frame_type = np.array(['miss' if miss else 'frame' for miss in misses])
    frame_type[full_marks > marks_threshold] = 'jump'
    per_frame_event_types = np.clip(per_frame_event_types, 0, 6) / 6
    df = pd.DataFrame.from_dict({'period length': periodLength, 
                                 'jumping speed': jumping_speed,
                                'jumps per second': jumps_per_second,
                                'periodicity': periodicity,
                                'miss': misses,
                                'frame_type': frame_type,
                                'event_type': per_frame_event_types,
                                'jumps': full_marks,
                                'jumps_size': (full_marks + 0.05) * 10,
                                'miss_size': np.clip((1 - periodicity) * 0.9 + 0.1, 1, 8),
                                'seconds': np.linspace(0, seconds, num=len(periodLength))})
    event_type_tick_vals = np.linspace(0, 1, num=7)
    event_type_colors = ['red', 'orange', 'green', 'blue', 'purple', 'pink', 'black']
    fig = px.scatter(data_frame=df,
                    x='seconds', 
                    y='jumps per second',
                    #symbol='frame_type',
                    #symbol_map={'frame': 'circle', 'miss': 'circle-open', 'jump': 'triangle-down'},
                    color='event_type',
                    size='jumps_size',
                    size_max=8,
                    color_continuous_scale=[(t, c) for t, c in zip(event_type_tick_vals, event_type_colors)],
                    range_color=(0,1),
                    title="Jumping speed (jumps-per-second)",
                    trendline='rolling',
                    trendline_options=dict(window=16),
                    trendline_color_override="goldenrod",
                    trendline_scope='overall',
                    template="plotly_dark")
    
    fig.update_layout(legend=dict(
            orientation="h",
            yanchor="bottom",
            y=0.98,
            xanchor="right",
            x=1,
            font=dict(
                family="Courier",
                size=12,
                color="black"
                ),
            bgcolor="AliceBlue",
        ),
        paper_bgcolor='rgba(0,0,0,0)',
        plot_bgcolor='rgba(0,0,0,0)'
    )
    # remove white outline from marks
    fig.update_traces(marker_line_width = 0)
    fig.update_layout(coloraxis_colorbar=dict(
        tickvals=event_type_tick_vals,
        ticktext=['single<br>rope', 'double<br>dutch', 'double<br>unders', 'single<br>bounces', 'double<br>bounces', 'triple<br>unders', 'other'],
        title='event type'
    ))

    hist = px.histogram(df, 
                        x="jumps per second", 
                        template="plotly_dark", 
                        marginal="box",
                        histnorm='percent',
                        title="Distribution of jumping speed (jumps-per-second)")
    
    # make a bar plot of the event type distribution

    bar = px.bar(x=['single rope', 'double dutch', 'double unders', 'single bounces', 'double bounces', 'triple unders', 'other'], 
                 y=event_type_probs,
                 template="plotly_dark",
                 title="Event Type Distribution",
                 labels={'x': 'event type', 'y': 'probability'},
                 range_y=[0, 1])

    return count_msg, fig, hist, bar
        

DESCRIPTION = '# NextJump 🦘'
DESCRIPTION += '\n## AI Counting for Competitive Jump Rope'
DESCRIPTION += '\nDemo created by [Dylan Plummer](https://dylan-plummer.github.io/). Check out the [NextJump iOS app](https://apps.apple.com/us/app/nextjump-jump-rope-counter/id6451026115).'


with gr.Blocks() as demo:
    gr.Markdown(DESCRIPTION)
    # in_video = gr.PlayableVideo(label="Input Video", elem_id='input-video', format='mp4', 
    #                             width=400, height=400, interactive=True, container=True,
    #                             max_length=150)
    with gr.Row():
        with gr.Column(min_width=480):
            video = gr.Video(label="Video Clip", elem_id='output-video', format='mp4', width=400, height=400)
            
    with gr.Row():
        run_button = gr.Button(value="Run", elem_id='run-button', scale=1)
        api_dummy_button = gr.Button(value="Run (No Viz)", elem_id='count-only', visible=False, scale=2)
        count_only = gr.Checkbox(label="Count Only", visible=False)
        api_token = gr.Textbox(label="API Key", elem_id='api-token', visible=False)

    with gr.Column(elem_id='output-video-container'):
        with gr.Row():
            with gr.Column():
                out_text = gr.Markdown(label="Predicted Count", elem_id='output-text')
                period_length = gr.Textbox(label="Period Length", elem_id='period-length', visible=False)
                periodicity = gr.Textbox(label="Periodicity", elem_id='periodicity', visible=False)
        with gr.Row():
            out_plot = gr.Plot(label="Jumping Speed", elem_id='output-plot')
        with gr.Row():
            with gr.Column():
                out_hist = gr.Plot(label="Speed Histogram", elem_id='output-hist')
            with gr.Column():
                out_event_type_dist = gr.Plot(label="Event Type Distribution", elem_id='output-event-type-dist')
              

    demo_inference = partial(inference, count_only_api=False, api_key=None)
    
    run_button.click(demo_inference, [video], outputs=[out_text, out_plot, out_hist, out_event_type_dist])
    api_inference = partial(inference, api_call=True)
    api_dummy_button.click(api_inference, [video, count_only, api_token], outputs=[period_length], api_name='inference')


if __name__ == "__main__":
    demo.queue(api_open=True, max_size=15).launch(share=False, pwa=True)