File size: 5,163 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 |
import mediapipe as mp
import cv2
from django.conf import settings
from .plank import PlankDetection
from .bicep_curl import BicepCurlDetection
from .squat import SquatDetection
from .lunge import LungeDetection
from .utils import rescale_frame
# Drawing helpers
mp_drawing = mp.solutions.drawing_utils
mp_pose = mp.solutions.pose
EXERCISE_DETECTIONS = None
def load_machine_learning_models():
"""Load all machine learning models"""
global EXERCISE_DETECTIONS
if EXERCISE_DETECTIONS is not None:
return
print("Loading ML models ...")
EXERCISE_DETECTIONS = {
"plank": PlankDetection(),
"bicep_curl": BicepCurlDetection(),
"squat": SquatDetection(),
"lunge": LungeDetection(),
}
def pose_detection(
video_file_path: str, video_name_to_save: str, rescale_percent: float = 40
):
"""Pose detection with MediaPipe Pose
Args:
video_file_path (str): path to video
video_name_to_save (str): path to save analyzed video
rescale_percent (float, optional): Percentage to scale back from the original video size. Defaults to 40.
"""
cap = cv2.VideoCapture(video_file_path)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) * rescale_percent / 100)
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) * rescale_percent / 100)
size = (width, height)
fps = int(cap.get(cv2.CAP_PROP_FPS))
fourcc = cv2.VideoWriter_fourcc(*"avc1")
save_to_path = f"{settings.MEDIA_ROOT}/{video_name_to_save}"
out = cv2.VideoWriter(save_to_path, fourcc, fps, size)
print("PROCESSING VIDEO ...")
with mp_pose.Pose(
min_detection_confidence=0.8, min_tracking_confidence=0.8
) as pose:
while cap.isOpened():
ret, image = cap.read()
if not ret:
break
image = rescale_frame(image, rescale_percent)
# Recolor image from BGR to RGB for mediapipe
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
results = pose.process(image)
# Recolor image from BGR to RGB for mediapipe
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
mp_drawing.draw_landmarks(
image,
results.pose_landmarks,
mp_pose.POSE_CONNECTIONS,
mp_drawing.DrawingSpec(
color=(244, 117, 66), thickness=2, circle_radius=2
),
mp_drawing.DrawingSpec(
color=(245, 66, 230), thickness=2, circle_radius=1
),
)
out.write(image)
print(f"PROCESSED, save to {save_to_path}.")
return
def exercise_detection(
video_file_path: str,
video_name_to_save: str,
exercise_type: str,
rescale_percent: float = 40,
) -> dict:
"""Analyzed Exercise Video
Args:
video_file_path (str): path to video
video_name_to_save (str): path to save analyzed video
exercise_type (str): exercise type
rescale_percent (float, optional): Percentage to scale back from the original video size. Defaults to 40.
Raises:
Exception: Not supported exercise type
Returns:
dict: Dictionary of analyzed stats from the video
"""
exercise_detection = EXERCISE_DETECTIONS.get(exercise_type)
if not exercise_detection:
raise Exception("Not supported exercise.")
cap = cv2.VideoCapture(video_file_path)
width = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH) * rescale_percent / 100)
height = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT) * rescale_percent / 100)
size = (width, height)
fps = int(cap.get(cv2.CAP_PROP_FPS))
frame_count = 0
fourcc = cv2.VideoWriter_fourcc(*"avc1")
saved_path = f"{settings.MEDIA_ROOT}/{video_name_to_save}"
out = cv2.VideoWriter(saved_path, fourcc, fps, size)
print("PROCESSING VIDEO ...")
with mp_pose.Pose(
min_detection_confidence=0.8, min_tracking_confidence=0.8
) as pose:
while cap.isOpened():
ret, image = cap.read()
if not ret:
break
# Calculate timestamp
frame_count += 1
timestamp = int(frame_count / fps)
image = rescale_frame(image, rescale_percent)
# Recolor image from BGR to RGB for mediapipe
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image.flags.writeable = False
results = pose.process(image)
# Recolor image from BGR to RGB for mediapipe
image.flags.writeable = True
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
if results.pose_landmarks:
exercise_detection.detect(
mp_results=results, image=image, timestamp=timestamp
)
out.write(image)
print(f"PROCESSED. Save path: {saved_path}")
processed_results = exercise_detection.handle_detected_results(
video_name=video_name_to_save
)
exercise_detection.clear_results()
return processed_results
|