Package to Calculate Human Body Measurements
Project description
Human Body Measurement via Video and Image
Install Package
$ pip install body-matrix
Package Usage
Load Models, Video and Image Frames Load Segmentation Model to CPU
from body_matrix import load
segmentation_model, segmentation_transforms = load.segmentation_model("cpu")
Load Segmentation Model to GPU
from body_matrix import load
segmentation_model, segmentation_transforms = load.segmentation_model("cuda")
Load Keypoints Model to CPU
from body_matrix import load
keypoints_model, keypoints_transforms = load.segmentation_model("cpu")
Load Keypoints Model to GPU
from body_matrix import load
keypoints_model, keypoints_transforms = load.segmentation_model("cuda")
Load Video
from body_matrix import load
video, frames_counts, fps, sample_frame = load.video("/Desktop/dt.mov", 90, 1)
print(frames_counts)
Load Image
from body_matrix import load
frames_path = load.image_frames("/Desktop/instagram")
print(frames_path)
Infer Detect_Main_Target
from body_matrix import infer
from body_matrix import load
keypoints_model, keypoints_transform = load.keypoints_model("cpu")
video, frame_counts, fps, sample_frame = load.video("04_01.mp4", -90, 1)
selected_box, keypoint = infer.detect_main_target(
sample_frame, "cpu", 0.8, keypoints_model, keypoints_transform
)
Segment_Selected_Target
from body_matrix import infer
from body_matrix import load
segment_model, segment_transform = load.segment_model("cpu")
video, frame_counts, fps, sample_frame = load.video("04_01.mp4", -90, 1)
selected_box, keypoint = infer.detect_main_target(
sample_frame, "cpu", 0.8, keypoints_model, keypoints_transform
)
mask, mask_image, bool_mask = infer.segment_selected_target(
sample_frame, "cpu", selected_box, 0.99, segment_model, segment_transform
)
Filter Keypoints_Filter
from body_matrix import load
from body_matrix import infer
from body_matrix import process
video, frame_counts, fps, sample_frame = load.video(
"sample02.mp4",
-90,
1
)
keypoints_model, keypoints_transform = load.keypoints_model("cpu")
boxes, keypoints = infer.detect_main_target(
sample_frame, "cpu", 0.8, keypoints_model, keypoints_transform
)
selected_kps = process.keypoints_filter(
['nose','left_shoulder','right_shoulder'],
keypoints
)
print(selected_kps[nose])
Human_Segmentation_Area
from body_matrix import load
from body_matrix import infer
from body_matrix import process
video, frame_counts, fps, sample_frame = load.video(
"sample02.mp4",
-90,
1
)
keypoints_model, keypoints_transform = load.keypoints_model("cpu")
boxes, keypoints = infer.detect_main_target(
sample_frame, "cpu", 0.8, keypoints_model, keypoints_transform
)
selected_kps = process.keypoints_filter(
['nose','left_shoulder','right_shoulder'],
keypoints
)
segment_model, segment_transform = load.segment_model("cpu")
mask, mask_image, bool_mask = infer.segment_selected_target(
sample_frame, "cpu", selected_box, 0.99, segment_model, segment_transform
)
segment_area = process.segmentation_area(
sample_frame,
bool_mask
)
Find_Segmentation_Intersection Find_Segmentation_Contour - TO be DONE Filter_Segmentation_Intersection
Measure Get_Box_Center_Coordinate Two_Boxes_Distance Box_Distance_From_Vertical_Line Box_Distance_From_Horizontal_Line Box_Distance_From_Center Two_Points_Distance Find_Middle_Point Find_Border_Length - TO be DONE Find_Polygon_Area - TO be DONE
Visualize Draw_Keypoint_Markers Draw_Circle_Label Draw_Rectangle_Label Draw_Connecting_Line
Score Find_Nearest_Value Find_Largest_Value Find_Best_Score
Export Generate_Video_From_Images Generate_Seeking_Video_From_Images Generate_Instagram_Video_From_Images Generate_Youtube_Video_From_Images
Project details
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