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import mediapipe as mp
import cv2
import numpy as np
import pandas as pd
import pickle
import traceback
from .utils import (
calculate_angle,
extract_important_keypoints,
get_static_file_url,
get_drawing_color,
)
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
class BicepPoseAnalysis:
def __init__(
self,
side: str,
stage_down_threshold: float,
stage_up_threshold: float,
peak_contraction_threshold: float,
loose_upper_arm_angle_threshold: float,
visibility_threshold: float,
):
# Initialize thresholds
self.stage_down_threshold = stage_down_threshold
self.stage_up_threshold = stage_up_threshold
self.peak_contraction_threshold = peak_contraction_threshold
self.loose_upper_arm_angle_threshold = loose_upper_arm_angle_threshold
self.visibility_threshold = visibility_threshold
self.side = side
self.counter = 0
self.stage = "down"
self.is_visible = True
self.detected_errors = {
"LOOSE_UPPER_ARM": 0,
"PEAK_CONTRACTION": 0,
}
# Params for loose upper arm error detection
self.loose_upper_arm = False
# Params for peak contraction error detection
self.peak_contraction_angle = 1000
def get_joints(self, landmarks) -> bool:
"""
Check for joints' visibility then get joints coordinate
"""
side = self.side.upper()
# Check visibility
joints_visibility = [
landmarks[mp_pose.PoseLandmark[f"{side}_SHOULDER"].value].visibility,
landmarks[mp_pose.PoseLandmark[f"{side}_ELBOW"].value].visibility,
landmarks[mp_pose.PoseLandmark[f"{side}_WRIST"].value].visibility,
]
is_visible = all([vis > self.visibility_threshold for vis in joints_visibility])
self.is_visible = is_visible
if not is_visible:
return self.is_visible
# Get joints' coordinates
self.shoulder = [
landmarks[mp_pose.PoseLandmark[f"{side}_SHOULDER"].value].x,
landmarks[mp_pose.PoseLandmark[f"{side}_SHOULDER"].value].y,
]
self.elbow = [
landmarks[mp_pose.PoseLandmark[f"{side}_ELBOW"].value].x,
landmarks[mp_pose.PoseLandmark[f"{side}_ELBOW"].value].y,
]
self.wrist = [
landmarks[mp_pose.PoseLandmark[f"{side}_WRIST"].value].x,
landmarks[mp_pose.PoseLandmark[f"{side}_WRIST"].value].y,
]
return self.is_visible
def analyze_pose(
self,
landmarks,
frame,
results,
timestamp: int,
lean_back_error: bool = False,
):
"""Analyze angles of an arm for error detection
Args:
landmarks (): MediaPipe Pose landmarks
frame (): OpenCV frame
results (): MediaPipe Pose results
timestamp (int): timestamp of the frame
lean_back_error (bool, optional): If there is an lean back error detected, ignore the analysis. Defaults to False.
Returns:
_type_: _description_
"""
has_error = False
self.get_joints(landmarks)
# Cancel calculation if visibility is poor
if not self.is_visible:
return (None, None, has_error)
# * Calculate curl angle for counter
bicep_curl_angle = int(calculate_angle(self.shoulder, self.elbow, self.wrist))
if bicep_curl_angle > self.stage_down_threshold:
self.stage = "down"
elif bicep_curl_angle < self.stage_up_threshold and self.stage == "down":
self.stage = "up"
self.counter += 1
# * Calculate the angle between the upper arm (shoulder & joint) and the Y axis
shoulder_projection = [
self.shoulder[0],
1,
] # Represent the projection of the shoulder to the X axis
ground_upper_arm_angle = int(
calculate_angle(self.elbow, self.shoulder, shoulder_projection)
)
# Stop analysis if lean back error is occur
if lean_back_error:
return (bicep_curl_angle, ground_upper_arm_angle, has_error)
# * Evaluation for LOOSE UPPER ARM error
if ground_upper_arm_angle > self.loose_upper_arm_angle_threshold:
has_error = True
cv2.rectangle(frame, (350, 0), (600, 40), (245, 117, 16), -1)
cv2.putText(
frame,
"ARM ERROR",
(360, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
frame,
"LOOSE UPPER ARM",
(355, 30),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
# Limit the saved frame
if not self.loose_upper_arm:
self.loose_upper_arm = True
self.detected_errors["LOOSE_UPPER_ARM"] += 1
results.append(
{"stage": "loose upper arm", "frame": frame, "timestamp": timestamp}
)
else:
self.loose_upper_arm = False
# * Evaluate PEAK CONTRACTION error
if self.stage == "up" and bicep_curl_angle < self.peak_contraction_angle:
# Save peaked contraction every rep
self.peak_contraction_angle = bicep_curl_angle
elif self.stage == "down":
# * Evaluate if the peak is higher than the threshold if True, marked as an error then saved that frame
if (
self.peak_contraction_angle != 1000
and self.peak_contraction_angle >= self.peak_contraction_threshold
):
cv2.rectangle(frame, (350, 0), (600, 40), (245, 117, 16), -1)
cv2.putText(
frame,
"ARM ERROR",
(360, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
frame,
"WEAK PEAK CONTRACTION",
(355, 30),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
self.detected_errors["PEAK_CONTRACTION"] += 1
results.append(
{
"stage": "peak contraction",
"frame": frame,
"timestamp": timestamp,
}
)
has_error = True
# Reset params
self.peak_contraction_angle = 1000
return (bicep_curl_angle, ground_upper_arm_angle, has_error)
def get_counter(self) -> int:
return self.counter
def reset(self):
self.counter = 0
self.stage = "down"
self.is_visible = True
self.detected_errors = {
"LOOSE_UPPER_ARM": 0,
"PEAK_CONTRACTION": 0,
}
# Params for loose upper arm error detection
self.loose_upper_arm = False
# Params for peak contraction error detection
self.peak_contraction_angle = 1000
class BicepCurlDetection:
ML_MODEL_PATH = get_static_file_url("model/bicep_curl_model.pkl")
INPUT_SCALER = get_static_file_url("model/bicep_curl_input_scaler.pkl")
VISIBILITY_THRESHOLD = 0.65
# Params for counter
STAGE_UP_THRESHOLD = 100
STAGE_DOWN_THRESHOLD = 120
# Params to catch FULL RANGE OF MOTION error
PEAK_CONTRACTION_THRESHOLD = 60
# LOOSE UPPER ARM error detection
LOOSE_UPPER_ARM = False
LOOSE_UPPER_ARM_ANGLE_THRESHOLD = 40
# STANDING POSTURE error detection
POSTURE_ERROR_THRESHOLD = 0.95
def __init__(self) -> None:
self.init_important_landmarks()
self.load_machine_learning_model()
self.left_arm_analysis = BicepPoseAnalysis(
side="left",
stage_down_threshold=self.STAGE_DOWN_THRESHOLD,
stage_up_threshold=self.STAGE_UP_THRESHOLD,
peak_contraction_threshold=self.PEAK_CONTRACTION_THRESHOLD,
loose_upper_arm_angle_threshold=self.LOOSE_UPPER_ARM_ANGLE_THRESHOLD,
visibility_threshold=self.VISIBILITY_THRESHOLD,
)
self.right_arm_analysis = BicepPoseAnalysis(
side="right",
stage_down_threshold=self.STAGE_DOWN_THRESHOLD,
stage_up_threshold=self.STAGE_UP_THRESHOLD,
peak_contraction_threshold=self.PEAK_CONTRACTION_THRESHOLD,
loose_upper_arm_angle_threshold=self.LOOSE_UPPER_ARM_ANGLE_THRESHOLD,
visibility_threshold=self.VISIBILITY_THRESHOLD,
)
self.stand_posture = 0
self.previous_stand_posture = 0
self.results = []
self.has_error = False
def init_important_landmarks(self) -> None:
"""
Determine Important landmarks for plank detection
"""
self.important_landmarks = [
"NOSE",
"LEFT_SHOULDER",
"RIGHT_SHOULDER",
"RIGHT_ELBOW",
"LEFT_ELBOW",
"RIGHT_WRIST",
"LEFT_WRIST",
"LEFT_HIP",
"RIGHT_HIP",
]
# 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 plank model")
try:
with open(self.ML_MODEL_PATH, "rb") as f:
self.model = pickle.load(f)
with open(self.INPUT_SCALER, "rb") as f2:
self.input_scaler = pickle.load(f2)
except Exception as e:
raise Exception(f"Error loading model, {e}")
def handle_detected_results(self, video_name: str) -> tuple:
"""
Save 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, {
"left_counter": self.left_arm_analysis.get_counter(),
"right_counter": self.right_arm_analysis.get_counter(),
}
def clear_results(self) -> None:
self.stand_posture = 0
self.previous_stand_posture = 0
self.results = []
self.has_error = False
self.right_arm_analysis.reset()
self.left_arm_analysis.reset()
def detect(
self,
mp_results,
image,
timestamp: int,
) -> None:
"""Error detection
Args:
mp_results (): MediaPipe results
image (): OpenCV image
timestamp (int): Current time of the frame
"""
self.has_error = False
try:
video_dimensions = [image.shape[1], image.shape[0]]
landmarks = mp_results.pose_landmarks.landmark
# * Model prediction for Lean-back error
# Extract keypoints from frame for the input
row = extract_important_keypoints(mp_results, self.important_landmarks)
X = pd.DataFrame(
[
row,
],
columns=self.headers[1:],
)
X = pd.DataFrame(self.input_scaler.transform(X))
# Make prediction and its probability
predicted_class = self.model.predict(X)[0]
prediction_probabilities = self.model.predict_proba(X)[0]
class_prediction_probability = round(
prediction_probabilities[np.argmax(prediction_probabilities)], 2
)
if class_prediction_probability >= self.POSTURE_ERROR_THRESHOLD:
self.stand_posture = predicted_class
# Stage management for saving results
if self.stand_posture == "L":
if self.previous_stand_posture == self.stand_posture:
pass
elif self.previous_stand_posture != self.stand_posture:
self.results.append(
{
"stage": "lean too far back",
"frame": image,
"timestamp": timestamp,
}
)
self.has_error = True
self.previous_stand_posture = self.stand_posture
# * Arms analysis for errors
# Left arm
(
left_bicep_curl_angle,
left_ground_upper_arm_angle,
left_arm_error,
) = self.left_arm_analysis.analyze_pose(
landmarks=landmarks,
frame=image,
results=self.results,
timestamp=timestamp,
lean_back_error=(self.stand_posture == "L"),
)
# Right arm
(
right_bicep_curl_angle,
right_ground_upper_arm_angle,
right_arm_error,
) = self.right_arm_analysis.analyze_pose(
landmarks=landmarks,
frame=image,
results=self.results,
timestamp=timestamp,
lean_back_error=(self.stand_posture == "L"),
)
self.has_error = (
True if (right_arm_error or left_arm_error) else self.has_error
)
# 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), (350, 40), (245, 117, 16), -1)
# Display probability
cv2.putText(
image,
"RIGHT",
(15, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
str(self.right_arm_analysis.counter)
if self.right_arm_analysis.is_visible
else "UNK",
(10, 30),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
# Display Left Counter
cv2.putText(
image,
"LEFT",
(95, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
str(self.left_arm_analysis.counter)
if self.left_arm_analysis.is_visible
else "UNK",
(100, 30),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
# Lean back error
cv2.putText(
image,
"Lean-Too-Far-Back",
(165, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
str("ERROR" if self.stand_posture == "L" else "CORRECT")
+ f", {predicted_class}, {class_prediction_probability}",
(160, 30),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
# * Visualize angles
# Visualize LEFT arm calculated angles
if self.left_arm_analysis.is_visible:
cv2.putText(
image,
str(left_bicep_curl_angle),
tuple(
np.multiply(
self.left_arm_analysis.elbow, video_dimensions
).astype(int)
),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
str(left_ground_upper_arm_angle),
tuple(
np.multiply(
self.left_arm_analysis.shoulder, video_dimensions
).astype(int)
),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 255),
1,
cv2.LINE_AA,
)
# Visualize RIGHT arm calculated angles
if self.right_arm_analysis.is_visible:
cv2.putText(
image,
str(right_bicep_curl_angle),
tuple(
np.multiply(
self.right_arm_analysis.elbow, video_dimensions
).astype(int)
),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
str(right_ground_upper_arm_angle),
tuple(
np.multiply(
self.right_arm_analysis.shoulder, video_dimensions
).astype(int)
),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(255, 255, 0),
1,
cv2.LINE_AA,
)
except Exception as e:
traceback.print_exc()
raise e
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