File size: 15,218 Bytes
369fac9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 |
import cv2
import mediapipe as mp
import numpy as np
import pandas as pd
import pickle
from .utils import (
calculate_distance,
extract_important_keypoints,
get_static_file_url,
get_drawing_color,
)
mp_pose = mp.solutions.pose
mp_drawing = mp.solutions.drawing_utils
def analyze_foot_knee_placement(
results,
stage: str,
foot_shoulder_ratio_thresholds: list,
knee_foot_ratio_thresholds: dict,
visibility_threshold: int,
) -> dict:
"""
Calculate the ratio between the foot and shoulder for FOOT PLACEMENT analysis
Calculate the ratio between the knee and foot for KNEE PLACEMENT analysis
Return result explanation:
-1: Unknown result due to poor visibility
0: Correct knee placement
1: Placement too tight
2: Placement too wide
"""
analyzed_results = {
"foot_placement": -1,
"knee_placement": -1,
}
landmarks = results.pose_landmarks.landmark
# * Visibility check of important landmarks for foot placement analysis
left_foot_index_vis = landmarks[
mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value
].visibility
right_foot_index_vis = landmarks[
mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value
].visibility
left_knee_vis = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].visibility
right_knee_vis = landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].visibility
# If visibility of any keypoints is low cancel the analysis
if (
left_foot_index_vis < visibility_threshold
or right_foot_index_vis < visibility_threshold
or left_knee_vis < visibility_threshold
or right_knee_vis < visibility_threshold
):
return analyzed_results
# * Calculate shoulder width
left_shoulder = [
landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_SHOULDER.value].y,
]
right_shoulder = [
landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_SHOULDER.value].y,
]
shoulder_width = calculate_distance(left_shoulder, right_shoulder)
# * Calculate 2-foot width
left_foot_index = [
landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_FOOT_INDEX.value].y,
]
right_foot_index = [
landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX.value].y,
]
foot_width = calculate_distance(left_foot_index, right_foot_index)
# * Calculate foot and shoulder ratio
foot_shoulder_ratio = round(foot_width / shoulder_width, 1)
# * Analyze FOOT PLACEMENT
min_ratio_foot_shoulder, max_ratio_foot_shoulder = foot_shoulder_ratio_thresholds
if min_ratio_foot_shoulder <= foot_shoulder_ratio <= max_ratio_foot_shoulder:
analyzed_results["foot_placement"] = 0
elif foot_shoulder_ratio < min_ratio_foot_shoulder:
analyzed_results["foot_placement"] = 1
elif foot_shoulder_ratio > max_ratio_foot_shoulder:
analyzed_results["foot_placement"] = 2
# * Visibility check of important landmarks for knee placement analysis
left_knee_vis = landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].visibility
right_knee_vis = landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].visibility
# If visibility of any keypoints is low cancel the analysis
if left_knee_vis < visibility_threshold or right_knee_vis < visibility_threshold:
print("Cannot see foot")
return analyzed_results
# * Calculate 2 knee width
left_knee = [
landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].x,
landmarks[mp_pose.PoseLandmark.LEFT_KNEE.value].y,
]
right_knee = [
landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].x,
landmarks[mp_pose.PoseLandmark.RIGHT_KNEE.value].y,
]
knee_width = calculate_distance(left_knee, right_knee)
# * Calculate foot and shoulder ratio
knee_foot_ratio = round(knee_width / foot_width, 1)
# * Analyze KNEE placement
up_min_ratio_knee_foot, up_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(
"up"
)
(
middle_min_ratio_knee_foot,
middle_max_ratio_knee_foot,
) = knee_foot_ratio_thresholds.get("middle")
down_min_ratio_knee_foot, down_max_ratio_knee_foot = knee_foot_ratio_thresholds.get(
"down"
)
if stage == "up":
if up_min_ratio_knee_foot <= knee_foot_ratio <= up_max_ratio_knee_foot:
analyzed_results["knee_placement"] = 0
elif knee_foot_ratio < up_min_ratio_knee_foot:
analyzed_results["knee_placement"] = 1
elif knee_foot_ratio > up_max_ratio_knee_foot:
analyzed_results["knee_placement"] = 2
elif stage == "middle":
if middle_min_ratio_knee_foot <= knee_foot_ratio <= middle_max_ratio_knee_foot:
analyzed_results["knee_placement"] = 0
elif knee_foot_ratio < middle_min_ratio_knee_foot:
analyzed_results["knee_placement"] = 1
elif knee_foot_ratio > middle_max_ratio_knee_foot:
analyzed_results["knee_placement"] = 2
elif stage == "down":
if down_min_ratio_knee_foot <= knee_foot_ratio <= down_max_ratio_knee_foot:
analyzed_results["knee_placement"] = 0
elif knee_foot_ratio < down_min_ratio_knee_foot:
analyzed_results["knee_placement"] = 1
elif knee_foot_ratio > down_max_ratio_knee_foot:
analyzed_results["knee_placement"] = 2
return analyzed_results
class SquatDetection:
ML_MODEL_PATH = get_static_file_url("model/squat_model.pkl")
PREDICTION_PROB_THRESHOLD = 0.7
VISIBILITY_THRESHOLD = 0.6
FOOT_SHOULDER_RATIO_THRESHOLDS = [1.2, 2.8]
KNEE_FOOT_RATIO_THRESHOLDS = {
"up": [0.5, 1.0],
"middle": [0.7, 1.0],
"down": [0.7, 1.1],
}
def __init__(self) -> None:
self.init_important_landmarks()
self.load_machine_learning_model()
self.current_stage = ""
self.previous_stage = {
"feet": "",
"knee": "",
}
self.counter = 0
self.results = []
self.has_error = False
def init_important_landmarks(self) -> None:
"""
Determine Important landmarks for squat detection
"""
self.important_landmarks = [
"NOSE",
"LEFT_SHOULDER",
"RIGHT_SHOULDER",
"LEFT_HIP",
"RIGHT_HIP",
"LEFT_KNEE",
"RIGHT_KNEE",
"LEFT_ANKLE",
"RIGHT_ANKLE",
]
# Generate all columns of the data frame
self.headers = ["label"] # Label column
for lm in self.important_landmarks:
self.headers += [
f"{lm.lower()}_x",
f"{lm.lower()}_y",
f"{lm.lower()}_z",
f"{lm.lower()}_v",
]
def load_machine_learning_model(self) -> None:
"""
Load machine learning model
"""
if not self.ML_MODEL_PATH:
raise Exception("Cannot found squat model")
try:
with open(self.ML_MODEL_PATH, "rb") as f:
self.model = pickle.load(f)
except Exception as e:
raise Exception(f"Error loading model, {e}")
def handle_detected_results(self, video_name: str) -> tuple:
"""
Save error frame as evidence
"""
file_name, _ = video_name.split(".")
save_folder = get_static_file_url("images")
for index, error in enumerate(self.results):
try:
image_name = f"{file_name}_{index}.jpg"
cv2.imwrite(f"{save_folder}/{file_name}_{index}.jpg", error["frame"])
self.results[index]["frame"] = image_name
except Exception as e:
print("ERROR cannot save frame: " + str(e))
self.results[index]["frame"] = None
return self.results, self.counter
def clear_results(self) -> None:
self.current_stage = ""
self.previous_stage = {
"feet": "",
"knee": "",
}
self.counter = 0
self.results = []
self.has_error = False
def detect(self, mp_results, image, timestamp) -> None:
"""
Make Squat Errors detection
"""
try:
# * Model prediction for SQUAT counter
# Extract keypoints from frame for the input
row = extract_important_keypoints(mp_results, self.important_landmarks)
X = pd.DataFrame([row], columns=self.headers[1:])
# Make prediction and its probability
predicted_class = self.model.predict(X)[0]
prediction_probabilities = self.model.predict_proba(X)[0]
prediction_probability = round(
prediction_probabilities[prediction_probabilities.argmax()], 2
)
# Evaluate model prediction
if (
predicted_class == "down"
and prediction_probability >= self.PREDICTION_PROB_THRESHOLD
):
self.current_stage = "down"
elif (
self.current_stage == "down"
and predicted_class == "up"
and prediction_probability >= self.PREDICTION_PROB_THRESHOLD
):
self.current_stage = "up"
self.counter += 1
# Analyze squat pose
analyzed_results = analyze_foot_knee_placement(
results=mp_results,
stage=self.current_stage,
foot_shoulder_ratio_thresholds=self.FOOT_SHOULDER_RATIO_THRESHOLDS,
knee_foot_ratio_thresholds=self.KNEE_FOOT_RATIO_THRESHOLDS,
visibility_threshold=self.VISIBILITY_THRESHOLD,
)
foot_placement_evaluation = analyzed_results["foot_placement"]
knee_placement_evaluation = analyzed_results["knee_placement"]
# * Evaluate FEET PLACEMENT error
if foot_placement_evaluation == -1:
feet_placement = "unknown"
elif foot_placement_evaluation == 0:
feet_placement = "correct"
elif foot_placement_evaluation == 1:
feet_placement = "too tight"
elif foot_placement_evaluation == 2:
feet_placement = "too wide"
# * Evaluate KNEE PLACEMENT error
if feet_placement == "correct":
if knee_placement_evaluation == -1:
knee_placement = "unknown"
elif knee_placement_evaluation == 0:
knee_placement = "correct"
elif knee_placement_evaluation == 1:
knee_placement = "too tight"
elif knee_placement_evaluation == 2:
knee_placement = "too wide"
else:
knee_placement = "unknown"
# Stage management for saving results
# * Feet placement
if feet_placement in ["too tight", "too wide"]:
# Stage not change
if self.previous_stage["feet"] == feet_placement:
pass
# Stage from correct to error
elif self.previous_stage["feet"] != feet_placement:
self.results.append(
{
"stage": f"feet {feet_placement}",
"frame": image,
"timestamp": timestamp,
}
)
self.previous_stage["feet"] = feet_placement
# * Knee placement
if knee_placement in ["too tight", "too wide"]:
# Stage not change
if self.previous_stage["knee"] == knee_placement:
pass
# Stage from correct to error
elif self.previous_stage["knee"] != knee_placement:
self.results.append(
{
"stage": f"knee {knee_placement}",
"frame": image,
"timestamp": timestamp,
}
)
self.previous_stage["knee"] = knee_placement
if feet_placement in ["too tight", "too wide"] or knee_placement in [
"too tight",
"too wide",
]:
self.has_error = True
else:
self.has_error = False
# Visualization
# Draw landmarks and connections
landmark_color, connection_color = get_drawing_color(self.has_error)
mp_drawing.draw_landmarks(
image,
mp_results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
mp_drawing.DrawingSpec(
color=landmark_color, thickness=2, circle_radius=2
),
mp_drawing.DrawingSpec(
color=connection_color, thickness=2, circle_radius=1
),
)
# Status box
cv2.rectangle(image, (0, 0), (300, 40), (245, 117, 16), -1)
# Display class
cv2.putText(
image,
"COUNT",
(10, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.3,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
f'{str(self.counter)}, {predicted_class.split(" ")[0]}, {str(prediction_probability)}',
(5, 25),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
# Display Feet and Shoulder width ratio
cv2.putText(
image,
"FEET",
(130, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.3,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
feet_placement,
(125, 25),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
# Display knee and Shoulder width ratio
cv2.putText(
image,
"KNEE",
(225, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.3,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
knee_placement,
(220, 25),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
except Exception as e:
print(f"Error while detecting squat errors: {e}")
|