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import cv2
import numpy as np
import pandas as pd
import pickle
import mediapipe as mp
from .utils import extract_important_keypoints, get_static_file_url, get_drawing_color
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
class PlankDetection:
ML_MODEL_PATH = get_static_file_url("model/plank_model.pkl")
INPUT_SCALER_PATH = get_static_file_url("model/plank_input_scaler.pkl")
PREDICTION_PROBABILITY_THRESHOLD = 0.6
def __init__(self) -> None:
self.init_important_landmarks()
self.load_machine_learning_model()
self.previous_stage = "unknown"
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",
"LEFT_ELBOW",
"RIGHT_ELBOW",
"LEFT_WRIST",
"RIGHT_WRIST",
"LEFT_HIP",
"RIGHT_HIP",
"LEFT_KNEE",
"RIGHT_KNEE",
"LEFT_ANKLE",
"RIGHT_ANKLE",
"LEFT_HEEL",
"RIGHT_HEEL",
"LEFT_FOOT_INDEX",
"RIGHT_FOOT_INDEX",
]
# 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 or not self.INPUT_SCALER_PATH:
raise Exception("Cannot found plank model file or input scaler file")
try:
with open(self.ML_MODEL_PATH, "rb") as f:
self.model = pickle.load(f)
with open(self.INPUT_SCALER_PATH, "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) -> None:
"""
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, self.previous_stage
def clear_results(self) -> None:
self.previous_stage = "unknown"
self.results = []
self.has_error = False
def detect(self, mp_results, image, timestamp) -> None:
"""
Make Plank Errors detection
"""
try:
# 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_probability = self.model.predict_proba(X)[0]
# Evaluate model prediction
if (
predicted_class == "C"
and prediction_probability[prediction_probability.argmax()]
>= self.PREDICTION_PROBABILITY_THRESHOLD
):
current_stage = "correct"
elif (
predicted_class == "L"
and prediction_probability[prediction_probability.argmax()]
>= self.PREDICTION_PROBABILITY_THRESHOLD
):
current_stage = "low back"
elif (
predicted_class == "H"
and prediction_probability[prediction_probability.argmax()]
>= self.PREDICTION_PROBABILITY_THRESHOLD
):
current_stage = "high back"
else:
current_stage = "unknown"
# Stage management for saving results
if current_stage in ["low back", "high back"]:
# Stage not change
if self.previous_stage == current_stage:
pass
# Stage from correct to error
elif self.previous_stage != current_stage:
self.results.append(
{"stage": current_stage, "frame": image, "timestamp": timestamp}
)
self.has_error = True
else:
self.has_error = False
self.previous_stage = current_stage
# 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), (250, 60), (245, 117, 16), -1)
# Display probability
cv2.putText(
image,
"PROB",
(15, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
str(
round(prediction_probability[np.argmax(prediction_probability)], 2)
),
(10, 40),
cv2.FONT_HERSHEY_COMPLEX,
1,
(255, 255, 255),
2,
cv2.LINE_AA,
)
# Display class
cv2.putText(
image,
"CLASS",
(95, 12),
cv2.FONT_HERSHEY_COMPLEX,
0.5,
(0, 0, 0),
1,
cv2.LINE_AA,
)
cv2.putText(
image,
current_stage,
(90, 40),
cv2.FONT_HERSHEY_COMPLEX,
1,
(255, 255, 255),
2,
cv2.LINE_AA,
)
except Exception as e:
raise Exception(f"Error while detecting plank errors: {e}")
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