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main.py
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main.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2022/6/15 15:06
# @Author : shen.p
# @Site :
# @File : main.py.py
# @Software: PyCharm
import time
import cv2
import numpy as np
from mediapipe.python.solutions import pose as mp_pose
from mediapipe.python.solutions import drawing_utils as mp_drawing
import os
import tqdm
import yaml
import datetime
# 指定视频路径和输出名称
from bodyclassfy import PoseClassifier
from count import RepetitionCounter
from data_process import show_image
from posture import FullBodyPoseEmbedder, EMADictSmoothing
from visual import PoseClassificationVisualizer
with open("./config.yml", "r", encoding='utf-8') as ymlfile:
cfg = yaml.load(ymlfile, Loader=yaml.SafeLoader)
video_path = cfg["input_path"]
class_name = cfg["specify_class"]
if video_path == 0:
strtime = time.strftime('%Y-%m-%d')
out_video_path = cfg["output_path"] + strtime + ".mp4"
else:
outname = video_path.split("/")[-1].split(".")
out_video_path = cfg["output_path"] + outname[0] + "_out." + outname[1]
video_cap = cv2.VideoCapture(video_path)
# Get some video parameters to generate output video with classificaiton.
video_n_frames = video_cap.get(cv2.CAP_PROP_FRAME_COUNT)
video_fps = video_cap.get(cv2.CAP_PROP_FPS)
video_width = int(video_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
video_height = int(video_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
if __name__ == '__main__':
# Folder with pose class CSVs. That should be the same folder you using while
# building classifier to output CSVs.
pose_samples_folder = cfg["pose_samples_folder"]
# Initialize tracker.
pose_tracker = mp_pose.Pose(upper_body_only=False)
# Initialize embedder.
pose_embedder = FullBodyPoseEmbedder()
# Initialize classifier.
# Ceck that you are using the same parameters as during bootstrapping.
pose_classifier = PoseClassifier(
pose_samples_folder=pose_samples_folder,
pose_embedder=pose_embedder,
top_n_by_max_distance=30,
top_n_by_mean_distance=10)
# # Uncomment to validate target poses used by classifier and find outliers.
# outliers = pose_classifier.find_pose_sample_outliers()
# print('Number of pose sample outliers (consider removing them): ', len(outliers))
# Initialize EMA smoothing.
pose_classification_filter = EMADictSmoothing(
window_size=10,
alpha=0.2)
# 指定动作的两个阈值
repetition_counter = RepetitionCounter(
class_name=class_name,
enter_threshold=cfg["enter_thread"],
exit_threshold=cfg["exit_thread"])
# Initialize renderer.
pose_classification_visualizer = PoseClassificationVisualizer(
class_name=class_name,
plot_x_max=video_n_frames,
# Graphic looks nicer if it's the same as `top_n_by_mean_distance`.
plot_y_max=10)
# Open output video.
out_video = cv2.VideoWriter(out_video_path, cv2.VideoWriter_fourcc(*'mp4v'), video_fps, (video_width, video_height))
frame_idx = 0
output_frame = None
with tqdm.tqdm(total=video_n_frames, position=0, leave=True) as pbar:
while True:
# Get next frame of the video.
success, input_frame = video_cap.read()
if not success:
break
# Run pose tracker.
input_frame = cv2.cvtColor(input_frame, cv2.COLOR_BGR2RGB)
result = pose_tracker.process(image=input_frame)
pose_landmarks = result.pose_landmarks
# Draw pose prediction.
output_frame = input_frame.copy()
if pose_landmarks is not None:
mp_drawing.draw_landmarks(
image=output_frame,
landmark_list=pose_landmarks,
connections=mp_pose.POSE_CONNECTIONS)
if pose_landmarks is not None:
# Get landmarks.
frame_height, frame_width = output_frame.shape[0], output_frame.shape[1]
pose_landmarks = np.array([[lmk.x * frame_width, lmk.y * frame_height, lmk.z * frame_width]
for lmk in pose_landmarks.landmark], dtype=np.float32)
assert pose_landmarks.shape == (33, 3), 'Unexpected landmarks shape: {}'.format(pose_landmarks.shape)
# Classify the pose on the current frame.
pose_classification = pose_classifier(pose_landmarks)
# Smooth classification using EMA.
pose_classification_filtered = pose_classification_filter(pose_classification)
# Count repetitions.
repetitions_count = repetition_counter(pose_classification_filtered)
else:
# No pose => no classification on current frame.
pose_classification = None
# Still add empty classification to the filter to maintaing correct
# smoothing for future frames.
pose_classification_filtered = pose_classification_filter(dict())
pose_classification_filtered = None
# Don't update the counter presuming that person is 'frozen'. Just
# take the latest repetitions count.
repetitions_count = repetition_counter.n_repeats
# Draw classification plot and repetition counter.
output_frame = pose_classification_visualizer(
frame=output_frame,
pose_classification=pose_classification,
pose_classification_filtered=pose_classification_filtered,
repetitions_count=repetitions_count)
# Save the output frame.
out_video.write(cv2.cvtColor(np.array(output_frame), cv2.COLOR_RGB2BGR))
# Show intermediate frames of the video to track progress.
if frame_idx % 50 == 0:
show_image(output_frame)
frame_idx += 1
pbar.update()
cv2.imshow('press q to break', cv2.cvtColor(np.array(output_frame), cv2.COLOR_RGB2BGR))
if cv2.waitKey(1) in [ord('q'), 27]: # 按键盘上的q或esc退出（在英文输入法下）
break
# Close output video.
out_video.release()
# Release MediaPipe resources.
pose_tracker.close()
# Show the last frame of the video.
if output_frame is not None:
show_image(output_frame)