import argparse
import csv
import os
import pickle
import re
import time
from threading import Thread
import cv2
import numpy as np
import pandas as pd
from mmdet.apis import DetInferencer, inference_detector, init_detector
# RTMpose
from mmpose.apis import inference_topdown
from mmpose.apis import init_model as init_pose_estimator
from mmpose.evaluation.functional import nms
from mmpose.registry import VISUALIZERS
from mmpose.structures import merge_data_samples
from mmpose.utils import adapt_mmdet_pipeline
# Pillow
from PIL import Image, ImageDraw
import config
# EARSNet
from modules.EARSNet.predictor import EARSNetPredictor
# Utilities
from util.calc_ste_position import CalcStethoscopePosition
from util.ears_ai import EarsAI
###############################################################################
# Config 値を参照
###############################################################################
CONV_COLOR = config.CONV_COLOR
XGBOOST_COLOR = config.XGBOOST_COLOR
LIGHTGBM_COLOR = config.LIGHTGBM_COLOR
EARSNET_COLOR = config.EARSNET_COLOR
CATBOOST_COLOR = config.CATBOOST_COLOR
NGBOOST_COLOR = config.NGBOOST_COLOR
CONV_ENABLED = config.CONV_ENABLED
XGBOOST_ENABLED = config.XGBOOST_ENABLED
LIGHTGBM_ENABLED = config.LIGHTGBM_ENABLED
CATBOOST_ENABLED = config.CATBOOST_ENABLED
NGBOOST_ENABLED = config.NGBOOST_ENABLED
POSENET_ENABLED = config.POSENET_ENABLED
RTMPOSE_ENABLED = config.RTMPOSE_ENABLED
MobileNetV1SSD_ENABLED = config.MOBILENETV1SSD_ENABLED
YOLOX_ENABLED = config.YOLOX_ENABLED
EARSNET_ENABLED = config.EARSNET_ENABLED
# ★ クロップ画像を使う EARSNet (別モデル) を使うかどうか
EARSNET_CROP_ENABLED = config.EARSNET_CROP_ENABLED
NORMALIZE_ENABLED = config.NORMALIZE_ENABLED
DEVICE = config.DEVICE # "cuda" or "cpu" など
###############################################################################
# リアルタイムFPS計測用のグローバル変数＆スレッド定義
###############################################################################
processed_frames = 0 # 処理済みフレーム数(メインスレッドでインクリメント)
stop_fps_thread = False # スレッド終了フラグ
fps_history = []
def fps_monitor(interval=1.0):
"""
別スレッドとして起動し、一定時間おきに processed_frames を確認してリアルタイムFPSを計算する。
interval=1.0 なら1秒ごとにFPSを出力。
"""
global processed_frames, stop_fps_thread, fps_history
last_count = 0
last_time = time.time()
while not stop_fps_thread:
time.sleep(interval)
now = time.time()
current_count = processed_frames
frames_delta = current_count - last_count
time_delta = now - last_time
if time_delta > 0:
current_fps = frames_delta / time_delta
else:
current_fps = 0.0
print(
f"[FPS Monitor] Real-time FPS: {current_fps:.2f} (frames: +{frames_delta})"
)
fps_history.append((now, current_fps))
last_count = current_count
last_time = now
###############################################################################
# モデルロード系
###############################################################################
def load_model(model_path, model_type="lgb"):
with open(model_path, "rb") as model_file:
return pickle.load(model_file)
def load_scaler(scaler_path):
with open(scaler_path, "rb") as f:
return pickle.load(f)
###############################################################################
# YOLOX
###############################################################################
def init_yolox():
try:
from mmengine.registry import DefaultScope
DefaultScope.get_instance("mmdet", scope_name="mmdet")
init_args = {
"model": config.YOLOX_CONFIG_FILE,
"weights": config.YOLOX_CHECKPOINT_FILE,
"device": DEVICE,
}
yolox_inferencer = DetInferencer(**init_args)
return yolox_inferencer
except Exception as e:
print(f"Error initializing YOLOX: {str(e)}")
return None
###############################################################################
# Pillow-based drawing helpers
###############################################################################
def pillow_draw_circle(draw, center, radius, fill=None, outline=None, width=1):
"""
Draw a circle (via ellipse) on a Pillow draw context.
center: (x, y)
radius: int
fill, outline: color tuples (R, G, B)
width: outline thickness if fill=None
"""
x, y = int(center[0]), int(center[1])
left_up = (x - radius, y - radius)
right_down = (x + radius, y + radius)
if fill is not None:
draw.ellipse([left_up, right_down], fill=fill, outline=outline, width=width)
else:
draw.ellipse([left_up, right_down], outline=outline, width=width)
def pillow_draw_polygon(draw, vertices, outline=(0, 255, 0), width=2):
"""
Draw a polygon (as connected lines) in Pillow.
vertices: list of (x, y)
"""
int_vertices = [(int(v[0]), int(v[1])) for v in vertices]
if len(int_vertices) > 1:
for i in range(len(int_vertices)):
j = (i + 1) % len(int_vertices)
draw.line([int_vertices[i], int_vertices[j]], fill=outline, width=width)
def pillow_draw_polyline(draw, points, color=(255, 0, 0), width=2):
"""
Draw connected lines for a list of points (like opencv polylines).
"""
if len(points) < 2:
return
int_points = [(int(p[0]), int(p[1])) for p in points]
for i in range(len(int_points) - 1):
draw.line([int_points[i], int_points[i + 1]], fill=color, width=width)
def draw_polygon_and_detection_pillow(
image, polygon_vertices, stethoscope_x, stethoscope_y
):
"""
Draw polygon & stethoscope location with Pillow, then return BGR np.array.
"""
# Convert to Pillow (BGR -> RGB)
pil_img = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
draw = ImageDraw.Draw(pil_img)
# polygon_vertices → [(x, y), ...] (int)
vertices = [(int(v[0]), int(v[1])) for v in polygon_vertices]
pillow_draw_polygon(draw, vertices, outline=(0, 255, 0), width=2)
if stethoscope_x is not None and stethoscope_y is not None:
x, y = int(stethoscope_x), int(stethoscope_y)
# Inner circle
pillow_draw_circle(draw, (x, y), 10, fill=(255, 0, 0))
# Outer circle
pillow_draw_circle(
draw, (x, y), 12, fill=None, outline=(255, 255, 255), width=2
)
# Convert back to BGR
out_img_rgb = np.array(pil_img)
out_img_bgr = cv2.cvtColor(out_img_rgb, cv2.COLOR_RGB2BGR)
return out_img_bgr
def yolox_detector_inference(frame, yolox_inferencer, pose_keypoints, score_thr=0.3):
"""YOLOXで聴診器を検出し、ポリゴン内部にある聴診器の中心座標を返す。"""
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
result = yolox_inferencer(inputs=frame_rgb, return_vis=True)
predictions = result["predictions"][0]
stethoscope_x = None
stethoscope_y = None
max_score = -1
nose = pose_keypoints[0]
left_shoulder = pose_keypoints[5]
right_shoulder = pose_keypoints[6]
left_hip = pose_keypoints[11]
right_hip = pose_keypoints[12]
expanded_left_shoulder, expanded_right_shoulder = expand_points(
left_shoulder, right_shoulder
)
expanded_left_hip, expanded_right_hip = expand_points(left_hip, right_hip)
polygon_vertices = np.array(
[
nose,
expanded_left_shoulder,
expanded_left_hip,
expanded_right_hip,
expanded_right_shoulder,
]
)
for i, (label, score) in enumerate(
zip(predictions["labels"], predictions["scores"])
):
if score >= score_thr and label == 0:
bbox = predictions["bboxes"][i]
center_x = (bbox[0] + bbox[2]) / 2
center_y = (bbox[1] + bbox[3]) / 2
if point_in_polygon([center_x, center_y], polygon_vertices):
if score > max_score:
stethoscope_x = center_x
stethoscope_y = center_y
max_score = score
if stethoscope_x is None or stethoscope_y is None:
stethoscope_x = 0
stethoscope_y = 0
# YOLOX の可視化出力 (RGB)
stethoscope_overlay_img = result["visualization"][0]
if (
len(stethoscope_overlay_img.shape) == 3
and stethoscope_overlay_img.shape[2] == 3
):
stethoscope_overlay_img = cv2.cvtColor(
stethoscope_overlay_img, cv2.COLOR_RGB2BGR
)
# Pillow描画
stethoscope_overlay_img = draw_polygon_and_detection_pillow(
stethoscope_overlay_img, polygon_vertices, stethoscope_x, stethoscope_y
)
return stethoscope_overlay_img, stethoscope_x, stethoscope_y
def expand_points(p1, p2):
mid_x = (p1[0] + p2[0]) / 2
mid_y = (p1[1] + p2[1]) / 2
vec_x = p1[0] - mid_x
vec_y = p1[1] - mid_y
new_p1 = [mid_x + vec_x * 2, mid_y + vec_y * 2]
new_p2 = [mid_x - vec_x * 2, mid_y - vec_y * 2]
return np.array(new_p1), np.array(new_p2)
def point_in_polygon(point, vertices):
x, y = point
n = len(vertices)
inside = False
j = n - 1
for i in range(n):
if (vertices[i][1] > y) != (vertices[j][1] > y):
slope = (vertices[j][0] - vertices[i][0]) / (
vertices[j][1] - vertices[i][1]
)
intersect_x = slope * (y - vertices[i][1]) + vertices[i][0]
if x < intersect_x:
inside = not inside
j = i
return inside
###############################################################################
# 各種座標変換
###############################################################################
def normalize_quadrilateral_with_point(points, extra_point):
all_points = np.vstack([points.reshape(-1, 2), extra_point])
center = np.mean(points.reshape(-1, 2), axis=0)
centered_points = all_points - center
shoulder_angle = calculate_rotation_angle(centered_points[0], centered_points[1])
hip_angle = calculate_rotation_angle(centered_points[2], centered_points[3])
average_angle = (shoulder_angle + hip_angle) / 2
rotation_matrix = np.array(
[
[np.cos(-average_angle), -np.sin(-average_angle)],
[np.sin(-average_angle), np.cos(-average_angle)],
]
)
rotated_points = np.dot(centered_points, rotation_matrix.T)
max_edge_length = np.max(
np.linalg.norm(
np.roll(rotated_points[:4], -1, axis=0) - rotated_points[:4], axis=1
)
)
if max_edge_length == 0:
return rotated_points
return rotated_points / max_edge_length
def calculate_rotation_angle(point1, point2):
vector = point2 - point1
return np.arctan2(vector[1], vector[0])
def video_to_frames(video_path, output_dir):
os.makedirs(output_dir, exist_ok=True)
video = cv2.VideoCapture(video_path)
if not video.isOpened():
raise IOError(f"Could not open video file: {video_path}")
frame_num = 0
while True:
success, frame = video.read()
if not success:
break
frame_num += 1
cv2.imwrite(os.path.join(output_dir, f"{frame_num}-frame.png"), frame)
video.release()
print(f"All frames saved to {output_dir}")
###############################################################################
# RTMpose キーポイント抽出
###############################################################################
def extract_keypoints_rtmpose(pose_results):
if not pose_results:
print("No pose results found.")
return None
max_avg_visible = 0
best_instance = None
for result in pose_results:
pred_instances = result.pred_instances
for instance in pred_instances:
avg_visible = np.mean(instance.keypoints_visible)
if avg_visible > max_avg_visible:
max_avg_visible = avg_visible
best_instance = instance
if best_instance is None:
print("No valid instances found.")
return None
keypoints = best_instance.keypoints[0]
return keypoints
###############################################################################
# 胴体クロップ生成
###############################################################################
def crop_body_from_keypoints(frame, left_shoulder, right_shoulder, left_hip, right_hip):
h, w, _ = frame.shape
xs = [left_shoulder[0], right_shoulder[0], left_hip[0], right_hip[0]]
ys = [left_shoulder[1], right_shoulder[1], left_hip[1], right_hip[1]]
xmin = int(min(xs))
xmax = int(max(xs))
ymin = int(min(ys))
ymax = int(max(ys))
margin = 20
xmin = max(0, xmin - margin)
xmax = min(w, xmax + margin)
ymin = max(0, ymin - margin)
ymax = min(h, ymax + margin)
cropped_frame = frame[ymin:ymax, xmin:xmax].copy()
return cropped_frame, (xmin, ymin)
###############################################################################
# メイン処理
###############################################################################
def process_images(args, detector, pose_estimator, visualizer):
global processed_frames
ears_ai = EarsAI()
calc_position = CalcStethoscopePosition()
base_dir = os.path.join(args.output_dir, "frames")
results_dir = os.path.join(args.output_dir, "results")
csv_path = os.path.join(results_dir, "results.csv")
normalized_csv_path = os.path.join(results_dir, "results-convert.csv")
pose_overlay_dir = os.path.join(results_dir, "pose_overlay_image")
stethoscope_overlay_dir = os.path.join(results_dir, "stethoscope_overlay_image")
cropped_dir = os.path.join(results_dir, "cropped_images")
os.makedirs(cropped_dir, exist_ok=True)
os.makedirs(results_dir, exist_ok=True)
os.makedirs(pose_overlay_dir, exist_ok=True)
os.makedirs(stethoscope_overlay_dir, exist_ok=True)
png_files = sorted(
[f for f in os.listdir(base_dir) if f.lower().endswith(".png")],
key=lambda x: int(re.search(r"(\d+)", x).group(1)),
)
print(f"Found {len(png_files)} PNG files in {base_dir}.")
rows = []
normalized_rows = []
yolox_inferencer = None
if YOLOX_ENABLED:
yolox_inferencer = init_yolox()
timings = {
"rtmpose_single": [],
"yolox_single": [],
"conv_single": [],
"lightgbm_single": [],
"xgboost_single": [],
"earsnet_single": [],
"earsnet_cropped_single": [],
"pipeline_rtmpose_yolox_conv": [],
"pipeline_rtmpose_yolox_lightgbm": [],
"pipeline_rtmpose_yolox_xgboost": [],
"pipeline_earsnet": [],
"pipeline_earsnet_cropped": [],
}
# モデルロード
if LIGHTGBM_ENABLED:
lgb_model_x = load_model("./models/LightGBM/stethoscope_calc_x_best_model.pkl")
lgb_model_y = load_model("./models/LightGBM/stethoscope_calc_y_best_model.pkl")
lgb_scaler_x = load_scaler("./models/LightGBM/scaler-x.pkl")
lgb_scaler_y = load_scaler("./models/LightGBM/scaler-y.pkl")
if XGBOOST_ENABLED:
xg_model_x = load_model("./models/XGBoost/stethoscope_calc_x_best_model.pkl")
xg_model_y = load_model("./models/XGBoost/stethoscope_calc_y_best_model.pkl")
xg_scaler_x = load_scaler("./models/XGBoost/scaler-x.pkl")
xg_scaler_y = load_scaler("./models/XGBoost/scaler-y.pkl")
if CATBOOST_ENABLED:
catboost_model_x = load_model(
"./models/CatBoost/stethoscope_calc_x_best_model.pkl"
)
catboost_model_y = load_model(
"./models/CatBoost/stethoscope_calc_y_best_model.pkl"
)
if NGBOOST_ENABLED:
ngboost_model_x = load_model(
"./models/NGBoost/stethoscope_calc_x_best_model.pkl"
)
ngboost_model_y = load_model(
"./models/NGBoost/stethoscope_calc_y_best_model.pkl"
)
if EARSNET_ENABLED:
earsnet_predictor = EARSNetPredictor(
weight_path="models/EARSNet/best_model.pth",
resnet_depth="18",
pretrained=True,
device=DEVICE,
)
if EARSNET_CROP_ENABLED:
earsnet_cropped_predictor = EARSNetPredictor(
weight_path="models/EARSNet/crop/best_model.pth",
resnet_depth="18",
pretrained=True,
device=DEVICE,
)
input_columns = [
"left_shoulder_x",
"left_shoulder_y",
"right_shoulder_x",
"right_shoulder_y",
"left_hip_x",
"left_hip_y",
"right_hip_x",
"right_hip_y",
"stethoscope_x",
"stethoscope_y",
]
# メインループ
for image_file_name in png_files:
image_path = os.path.join(base_dir, image_file_name)
frame = cv2.imread(image_path)
if frame is None:
print(f"Failed to load image: {image_path}")
continue
rtmpose_time = 0.0
if RTMPOSE_ENABLED:
start_time_rtmpose = time.time()
frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
det_result = inference_detector(detector, frame_rgb)
pred_instance = det_result.pred_instances.cpu().numpy()
bboxes = np.concatenate(
(pred_instance.bboxes, pred_instance.scores[:, None]), axis=1
)
bboxes = bboxes[
np.logical_and(pred_instance.labels == 0, pred_instance.scores > 0.3)
]
bboxes = bboxes[nms(bboxes, 0.3), :4]
pose_results = inference_topdown(pose_estimator, frame_rgb, bboxes)
data_samples = merge_data_samples(pose_results)
pose_keypoints = extract_keypoints_rtmpose(pose_results)
end_time_rtmpose = time.time()
rtmpose_time = end_time_rtmpose - start_time_rtmpose
timings["rtmpose_single"].append(rtmpose_time)
if pose_keypoints is None:
print(f"Failed to extract keypoints for image: {image_path}")
processed_frames += 1
continue
if visualizer is not None:
visualizer.add_datasample(
"result",
frame_rgb,
data_sample=data_samples,
draw_gt=False,
draw_heatmap=False,
draw_bbox=False,
show_kpt_idx=False,
skeleton_style="mmpose",
show=False,
wait_time=0,
kpt_thr=0.3,
)
pose_overlay_img = visualizer.get_image()
pose_overlay_bgr = cv2.cvtColor(pose_overlay_img, cv2.COLOR_RGB2BGR)
cv2.imwrite(
os.path.join(pose_overlay_dir, image_file_name), pose_overlay_bgr
)
left_shoulder = (pose_keypoints[5][0], pose_keypoints[5][1])
right_shoulder = (pose_keypoints[6][0], pose_keypoints[6][1])
left_hip = (pose_keypoints[11][0], pose_keypoints[11][1])
right_hip = (pose_keypoints[12][0], pose_keypoints[12][1])
elif POSENET_ENABLED:
start_time_rtmpose = time.time()
pose_overlay_img, *landmarks = ears_ai.pose_detect(frame, None)
end_time_rtmpose = time.time()
rtmpose_time = end_time_rtmpose - start_time_rtmpose
timings["rtmpose_single"].append(rtmpose_time)
left_shoulder = landmarks[0]
right_shoulder = landmarks[1]
left_hip = landmarks[2]
right_hip = landmarks[3]
cv2.imwrite(
os.path.join(pose_overlay_dir, image_file_name), pose_overlay_img
)
else:
left_shoulder = (0, 0)
right_shoulder = (0, 0)
left_hip = (0, 0)
right_hip = (0, 0)
# YOLOX
yolox_time = 0.0
stethoscope_x, stethoscope_y = 0, 0
if YOLOX_ENABLED:
if (
RTMPOSE_ENABLED
and "pose_keypoints" in locals()
and pose_keypoints is not None
):
start_time_yolox = time.time()
stethoscope_overlay_img, stethoscope_x, stethoscope_y = (
yolox_detector_inference(frame, yolox_inferencer, pose_keypoints)
)
end_time_yolox = time.time()
yolox_time = end_time_yolox - start_time_yolox
timings["yolox_single"].append(yolox_time)
cv2.imwrite(
os.path.join(stethoscope_overlay_dir, image_file_name),
stethoscope_overlay_img,
)
elif POSENET_ENABLED:
pose_keypoints_pose_net = [[0, 0]] * 13
pose_keypoints_pose_net[5] = (left_shoulder[0], left_shoulder[1])
pose_keypoints_pose_net[6] = (right_shoulder[0], right_shoulder[1])
pose_keypoints_pose_net[11] = (left_hip[0], left_hip[1])
pose_keypoints_pose_net[12] = (right_hip[0], right_hip[1])
start_time_yolox = time.time()
stethoscope_overlay_img, stethoscope_x, stethoscope_y = (
yolox_detector_inference(
frame, yolox_inferencer, pose_keypoints_pose_net
)
)
end_time_yolox = time.time()
yolox_time = end_time_yolox - start_time_yolox
timings["yolox_single"].append(yolox_time)
cv2.imwrite(
os.path.join(stethoscope_overlay_dir, image_file_name),
stethoscope_overlay_img,
)
detection_time_rtmpose_yolox = rtmpose_time + yolox_time
row = {
"image_file_name": image_file_name,
"left_shoulder_x": left_shoulder[0],
"left_shoulder_y": left_shoulder[1],
"right_shoulder_x": right_shoulder[0],
"right_shoulder_y": right_shoulder[1],
"left_hip_x": left_hip[0],
"left_hip_y": left_hip[1],
"right_hip_x": right_hip[0],
"right_hip_y": right_hip[1],
"stethoscope_x": stethoscope_x,
"stethoscope_y": stethoscope_y,
}
if EARSNET_ENABLED:
start_time_earsnet = time.time()
earsnet_x, earsnet_y = earsnet_predictor.predict(image_path)
end_time_earsnet = time.time()
earsnet_time = end_time_earsnet - start_time_earsnet
timings["earsnet_single"].append(earsnet_time)
timings["pipeline_earsnet"].append(earsnet_time)
row["earsnet_stethoscope_x"] = earsnet_x
row["earsnet_stethoscope_y"] = earsnet_y
if EARSNET_CROP_ENABLED:
cropped_img, (crop_xmin, crop_ymin) = crop_body_from_keypoints(
frame, left_shoulder, right_shoulder, left_hip, right_hip
)
cropped_filename = os.path.splitext(image_file_name)[0] + "_cropped.png"
cv2.imwrite(os.path.join(cropped_dir, cropped_filename), cropped_img)
start_time_earsnet_cropped = time.time()
earsnet_cropped_x, earsnet_cropped_y = earsnet_cropped_predictor.predict(
os.path.join(cropped_dir, cropped_filename)
)
end_time_earsnet_cropped = time.time()
earsnet_cropped_time = end_time_earsnet_cropped - start_time_earsnet_cropped
timings["earsnet_cropped_single"].append(earsnet_cropped_time)
pipeline_earsnet_cropped_time = rtmpose_time + earsnet_cropped_time
timings["pipeline_earsnet_cropped"].append(pipeline_earsnet_cropped_time)
row["earsnet_crop_stethoscope_x"] = earsnet_cropped_x
row["earsnet_crop_stethoscope_y"] = earsnet_cropped_y
# 正規化
source_points = np.array(
[
[float(row["left_shoulder_x"]), float(row["left_shoulder_y"])],
[float(row["right_shoulder_x"]), float(row["right_shoulder_y"])],
[float(row["left_hip_x"]), float(row["left_hip_y"])],
[float(row["right_hip_x"]), float(row["right_hip_y"])],
],
dtype=np.float32,
)
stethoscope_point = np.array(
[float(row["stethoscope_x"]), float(row["stethoscope_y"])]
)
normalized_points = normalize_quadrilateral_with_point(
source_points.flatten(), stethoscope_point
)
normalized_row = {
"image_file_name": image_file_name,
"left_shoulder_x": normalized_points[0, 0],
"left_shoulder_y": normalized_points[0, 1],
"right_shoulder_x": normalized_points[1, 0],
"right_shoulder_y": normalized_points[1, 1],
"left_hip_x": normalized_points[2, 0],
"left_hip_y": normalized_points[2, 1],
"right_hip_x": normalized_points[3, 0],
"right_hip_y": normalized_points[3, 1],
"stethoscope_x": normalized_points[4, 0],
"stethoscope_y": normalized_points[4, 1],
}
if EARSNET_ENABLED:
stetho_point_earsnet = np.array(
[
float(row.get("earsnet_stethoscope_x", 0)),
float(row.get("earsnet_stethoscope_y", 0)),
]
)
norm_earsnet = normalize_quadrilateral_with_point(
source_points.flatten(), stetho_point_earsnet
)
normalized_row["earsnet_stethoscope_x"] = norm_earsnet[4, 0]
normalized_row["earsnet_stethoscope_y"] = norm_earsnet[4, 1]
if EARSNET_CROP_ENABLED:
stetho_point_crop = np.array(
[
float(row.get("earsnet_crop_stethoscope_x", 0)),
float(row.get("earsnet_crop_stethoscope_y", 0)),
]
)
norm_earsnet_crop = normalize_quadrilateral_with_point(
source_points.flatten(), stetho_point_crop
)
normalized_row["earsnet_crop_stethoscope_x"] = norm_earsnet_crop[4, 0]
normalized_row["earsnet_crop_stethoscope_y"] = norm_earsnet_crop[4, 1]
rows.append(row)
normalized_rows.append(normalized_row)
# パイプライン
if RTMPOSE_ENABLED and YOLOX_ENABLED:
if CONV_ENABLED:
start_conv = time.time()
source_pts = np.array(
[
[float(row[f"{pos}_x"]), float(row[f"{pos}_y"])]
for pos in [
"left_shoulder",
"right_shoulder",
"left_hip",
"right_hip",
]
],
dtype=np.float32,
)
stetho_pt = np.array(
[float(row["stethoscope_x"]), float(row["stethoscope_y"])]
)
_ = calc_position.calc_affine(source_pts, *stetho_pt)
end_conv = time.time()
conv_time = end_conv - start_conv
timings["conv_single"].append(conv_time)
timings["pipeline_rtmpose_yolox_conv"].append(
detection_time_rtmpose_yolox + conv_time
)
if XGBOOST_ENABLED:
xg_start = time.time()
if NORMALIZE_ENABLED:
input_data_xg = pd.DataFrame([normalized_rows[-1]])
else:
input_data_xg = pd.DataFrame([rows[-1]])
X_scaled_x = xg_scaler_x.transform(input_data_xg[input_columns])
_ = xg_model_x.predict(X_scaled_x)[0]
X_scaled_y = xg_scaler_y.transform(input_data_xg[input_columns])
_ = xg_model_y.predict(X_scaled_y)[0]
xg_end = time.time()
xg_time = xg_end - xg_start
timings["xgboost_single"].append(xg_time)
timings["pipeline_rtmpose_yolox_xgboost"].append(
detection_time_rtmpose_yolox + xg_time
)
if LIGHTGBM_ENABLED:
lgb_start = time.time()
if NORMALIZE_ENABLED:
input_data_lgb = pd.DataFrame([normalized_rows[-1]])
else:
input_data_lgb = pd.DataFrame([rows[-1]])
X_scaled_x = lgb_scaler_x.transform(input_data_lgb[input_columns])
_ = lgb_model_x.predict(X_scaled_x)[0]
X_scaled_y = lgb_scaler_y.transform(input_data_lgb[input_columns])
_ = lgb_model_y.predict(X_scaled_y)[0]
lgb_end = time.time()
lgb_time = lgb_end - lgb_start
timings["lightgbm_single"].append(lgb_time)
timings["pipeline_rtmpose_yolox_lightgbm"].append(
detection_time_rtmpose_yolox + lgb_time
)
processed_frames += 1
# CSV 書き込み
if rows:
fieldnames = list(rows[0].keys())
csvfile_path = os.path.join(results_dir, "results.csv")
normfile_path = os.path.join(results_dir, "results-convert.csv")
os.makedirs(results_dir, exist_ok=True)
with (
open(csvfile_path, "w", newline="") as csvfile,
open(normfile_path, "w", newline="") as norm_csvfile,
):
writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
writer.writeheader()
norm_fieldnames = list(normalized_rows[0].keys())
norm_writer = csv.DictWriter(norm_csvfile, fieldnames=norm_fieldnames)
norm_writer.writeheader()
for row_, norm_row_ in zip(rows, normalized_rows):
writer.writerow(row_)
norm_writer.writerow(norm_row_)
print(f"Processed and saved results to: {csvfile_path}")
print(f"Processed and saved normalized results to: {normfile_path}")
generate_visualizations(csvfile_path, base_dir, results_dir)
else:
print("No data to write to CSV.")
# FPS計測
fps_data = []
for method_name, time_list in timings.items():
if not time_list:
continue
total_time = sum(time_list)
num_calls = len(time_list)
avg_time = total_time / num_calls if num_calls > 0 else 0
fps = 1.0 / avg_time if avg_time > 0 else 0
fps_data.append(
{
"method_name": method_name,
"num_calls": num_calls,
"total_time_sec": f"{total_time:.6f}",
"avg_time_sec": f"{avg_time:.6f}",
"fps": f"{fps:.2f}",
}
)
fps_csv_path = os.path.join(results_dir, "fps_results.csv")
with open(fps_csv_path, "w", newline="") as f:
writer = csv.DictWriter(
f,
fieldnames=[
"method_name",
"num_calls",
"total_time_sec",
"avg_time_sec",
"fps",
],
)
writer.writeheader()
for rowf in fps_data:
writer.writerow(rowf)
print("\n===== FPS Results (subcomponent & pipeline) =====")
for rowf in fps_data:
print(
f"{rowf['method_name']}: calls={rowf['num_calls']}, "
f"total={rowf['total_time_sec']}s, avg={rowf['avg_time_sec']}s, FPS={rowf['fps']}"
)
###############################################################################
# 可視化・動画化（Body画像への描画も Pillow で行う）
###############################################################################
def generate_visualizations(csv_path, original_images_dir, results_dir):
"""
CSVに書き込んだ推定結果を用い、BodyF.png(or BodyB.png)への描画や動画化を行う。
すべての描画をPillowで実装。最終的にcv2.imwrite()で保存するのでRGB→BGR変換が必要。
"""
df = pd.read_csv(csv_path)
body_image_path = "./images/body/BodyF.png"
if not os.path.exists(body_image_path):
print(f"Warning: {body_image_path} not found.")
return
# Pillow (RGB)
body_img_pil = Image.open(body_image_path).convert("RGB")
# np.array()すると「RGB順」のまま入る
body_np_rgb = np.array(body_img_pil)
dirs = {"marked": "marked_images"}
if CONV_ENABLED:
dirs["conv"] = "conv"
if XGBOOST_ENABLED:
dirs["Xgboost"] = "Xgboost"
if LIGHTGBM_ENABLED:
dirs["lightGBM"] = "lightGBM"
if CATBOOST_ENABLED:
dirs["catboost"] = "catboost"
if NGBOOST_ENABLED:
dirs["ngboost"] = "ngboost"
if EARSNET_ENABLED:
dirs["earsnet"] = "earsnet"
if EARSNET_CROP_ENABLED:
dirs["earsnet_crop"] = "earsnet_crop"
dirs["combined"] = "combined"
os.makedirs(os.path.join(results_dir, "marked_images"), exist_ok=True)
for key in dirs:
if key != "marked":
os.makedirs(
os.path.join(results_dir, f"{dirs[key]}_with_trajectory"), exist_ok=True
)
os.makedirs(
os.path.join(results_dir, f"{dirs[key]}_without_trajectory"),
exist_ok=True,
)
points = {key: [] for key in dirs.keys() if key not in ["marked", "combined"]}
colors = {
"conv": CONV_COLOR,
"Xgboost": XGBOOST_COLOR,
"lightGBM": LIGHTGBM_COLOR,
"catboost": CATBOOST_COLOR,
"ngboost": NGBOOST_COLOR,
"earsnet": EARSNET_COLOR,
"earsnet_crop": (255, 51, 255),
}
for _, row in df.iterrows():
original_image_path = os.path.join(original_images_dir, row["image_file_name"])
if not os.path.exists(original_image_path):
continue
original_image = cv2.imread(original_image_path)
if original_image is None:
continue
# --- 1) マーキング ---
pil_marked = Image.fromarray(cv2.cvtColor(original_image, cv2.COLOR_BGR2RGB))
draw_marked = ImageDraw.Draw(pil_marked)
for point in [
"left_shoulder",
"right_shoulder",
"left_hip",
"right_hip",
"stethoscope",
]:
col_x = f"{point}_x"
col_y = f"{point}_y"
if col_x in row and col_y in row:
val_x = row[col_x]
val_y = row[col_y]
if pd.isna(val_x) or pd.isna(val_y):
continue
x, y = int(val_x), int(val_y)
pillow_draw_circle(draw_marked, (x, y), 5, fill=(255, 255, 0))
# Pillow(RGB) → BGR
marked_rgb = np.array(pil_marked)
marked_bgr = cv2.cvtColor(marked_rgb, cv2.COLOR_RGB2BGR)
marked_dir = os.path.join(results_dir, "marked_images")
cv2.imwrite(os.path.join(marked_dir, row["image_file_name"]), marked_bgr)
# --- 2) BodyF.pngに軌跡描画 ---
# body_np_rgbをコピー (RGB配列)
combined_image_with_traj_rgb = body_np_rgb.copy()
combined_image_without_traj_rgb = body_np_rgb.copy()
pil_with_traj = Image.fromarray(combined_image_with_traj_rgb)
pil_without_traj = Image.fromarray(combined_image_without_traj_rgb)
draw_with_traj = ImageDraw.Draw(pil_with_traj)
draw_without_traj = ImageDraw.Draw(pil_without_traj)
for key in points.keys():
col_x = f"{key}_stethoscope_x"
col_y = f"{key}_stethoscope_y"
if col_x not in row or col_y not in row:
continue
val_x = row[col_x]
val_y = row[col_y]
if pd.isna(val_x) or pd.isna(val_y):
continue
x, y = int(val_x), int(val_y)
points[key].append((x, y))
color = colors[key] if key in colors else (0, 0, 255)
# 個別 with trajectory
indiv_with_traj_rgb = body_np_rgb.copy()
pil_indiv_with = Image.fromarray(indiv_with_traj_rgb)
draw_indiv_with = ImageDraw.Draw(pil_indiv_with)
if len(points[key]) > 1:
pillow_draw_polyline(draw_indiv_with, points[key], color=color, width=2)
pillow_draw_circle(draw_indiv_with, (x, y), 10, fill=color)
indiv_with_traj_rgb2 = np.array(pil_indiv_with)
# RGB -> BGR
indiv_with_traj_bgr = cv2.cvtColor(indiv_with_traj_rgb2, cv2.COLOR_RGB2BGR)
out_path_with = os.path.join(
results_dir, f"{dirs[key]}_with_trajectory", row["image_file_name"]
)
cv2.imwrite(out_path_with, indiv_with_traj_bgr)
# 個別 without trajectory
indiv_without_traj_rgb = body_np_rgb.copy()
pil_indiv_without = Image.fromarray(indiv_without_traj_rgb)
draw_indiv_without = ImageDraw.Draw(pil_indiv_without)
pillow_draw_circle(draw_indiv_without, (x, y), 10, fill=color)
indiv_without_traj_rgb2 = np.array(pil_indiv_without)
indiv_without_traj_bgr = cv2.cvtColor(
indiv_without_traj_rgb2, cv2.COLOR_RGB2BGR
)
out_path_without = os.path.join(
results_dir, f"{dirs[key]}_without_trajectory", row["image_file_name"]
)
cv2.imwrite(out_path_without, indiv_without_traj_bgr)
# combined with trajectory
if len(points[key]) > 1:
pillow_draw_polyline(draw_with_traj, points[key], color=color, width=2)
pillow_draw_circle(draw_with_traj, (x, y), 10, fill=color)
# combined without trajectory
pillow_draw_circle(draw_without_traj, (x, y), 10, fill=color)
# 結果 (pil_with_traj / pil_without_traj) を BGR に変換して保存
combined_with_traj_rgb2 = np.array(pil_with_traj) # RGB
combined_without_traj_rgb2 = np.array(pil_without_traj) # RGB
combined_with_traj_bgr = cv2.cvtColor(
combined_with_traj_rgb2, cv2.COLOR_RGB2BGR
)
combined_without_traj_bgr = cv2.cvtColor(
combined_without_traj_rgb2, cv2.COLOR_RGB2BGR
)
os.makedirs(
os.path.join(results_dir, "combined_with_trajectory"), exist_ok=True
)
os.makedirs(
os.path.join(results_dir, "combined_without_trajectory"), exist_ok=True
)
cv2.imwrite(
os.path.join(
results_dir, "combined_with_trajectory", row["image_file_name"]
),
combined_with_traj_bgr,
)
cv2.imwrite(
os.path.join(
results_dir, "combined_without_trajectory", row["image_file_name"]
),
combined_without_traj_bgr,
)
# 動画化
create_video_from_images(
os.path.join(results_dir, "marked_images"),
os.path.join(results_dir, "marked_video.mp4"),
)
for key in dirs:
if key not in ["marked", "combined"]:
create_video_from_images(
os.path.join(results_dir, f"{dirs[key]}_with_trajectory"),
os.path.join(results_dir, f"{key}_video_with_trajectory.mp4"),
)
create_video_from_images(
os.path.join(results_dir, f"{dirs[key]}_without_trajectory"),
os.path.join(results_dir, f"{key}_video_without_trajectory.mp4"),
)
def create_video_from_images(image_dir, output_path):
if not os.path.exists(image_dir):
return
images = sorted(
[img for img in os.listdir(image_dir) if img.endswith(".png")],
key=lambda x: int(re.search(r"(\d+)", x).group()),
)
if not images:
print(f"No images found in {image_dir}")
return
frame = cv2.imread(os.path.join(image_dir, images[0]))
if frame is None:
print(f"Failed to read the first image in {image_dir}")
return
height, width, _ = frame.shape
video = cv2.VideoWriter(
output_path, cv2.VideoWriter_fourcc(*"mp4v"), 30, (width, height)
)
for image in images:
img_path = os.path.join(image_dir, image)
img = cv2.imread(img_path)
if img is not None:
video.write(img)
video.release()
print(f"Created video: {output_path}")
def main():
parser = argparse.ArgumentParser(description="Process video and generate results.")
parser.add_argument(
"--video_path",
default="./video/tes.mp4",
help="Path to the input video file",
)
parser.add_argument(
"--output_dir",
default="output",
help="Directory to save output images and results",
)
det_config = "modules/rtmpose/mmdetection_cfg/rtmdet_m_640-8xb32_coco-person.py"
det_checkpoint = (
"models/rtmpose/rtmdet_m_8xb32-100e_coco-obj365-person-235e8209.pth"
)
pose_config = (
"modules/rtmpose/configs/body_2d_keypoint/rtmpose/body8/"
"rtmpose-l_8xb256-420e_body8-256x192.py"
)
pose_checkpoint = "models/rtmpose/rtmpose-l_simcc-aic-coco_pt-aic-coco_420e-256x192-f016ffe0_20230126.pth"
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
fps_thread = Thread(target=fps_monitor, args=(1.0,), daemon=True)
fps_thread.start()
frames_dir = os.path.join(args.output_dir, "frames")
video_to_frames(args.video_path, frames_dir)
if RTMPOSE_ENABLED:
detector = init_detector(det_config, det_checkpoint, device=DEVICE)
detector.cfg = adapt_mmdet_pipeline(detector.cfg)
pose_estimator = init_pose_estimator(
pose_config, pose_checkpoint, device=DEVICE
)
visualizer = VISUALIZERS.build(pose_estimator.cfg.visualizer)
visualizer.set_dataset_meta(
pose_estimator.dataset_meta, skeleton_style="mmpose"
)
process_images(args, detector, pose_estimator, visualizer)
else:
process_images(args, None, None, None)
global stop_fps_thread
stop_fps_thread = True
fps_thread.join()
print("All done.")
if __name__ == "__main__":
main()
