body-matrix 0.0.5

Package to Calculate Human Body Measurements

Human Body Measurement via Video and Image

Install the 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")
selected_box, 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 - TO be DONE Find_Segmentation_Contour - TO be DONE Filter_Segmentation_Intersection - TO be DONE

Draw

Keypoint_Markers

from body_matrix import load
from body_matrix import infer
from body_matrix import process
from body_matrix import draw

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
)

output = draw.keypoint_markers(
    coordinates=selected_kps,
    image=sample_frame, 
    label_font="/path/Roboto-Bold.ttf"
)

output

Circle_Label

from body_matrix import draw

middle_back = (309.86787033081055, 699.3268127441406)
updated_sample = draw.circle_label(
    image=output, 
    central_point=middle_back,
    radius=100,
    background_color = "#FF0000", 
    label = "21", 
    label_size = 100,
    label_font="/path/Roboto-Bold.ttf", 
    label_color ="#FAFF00"
)

Rectangle_Label

from body_matrix import draw
from body_matrix import load

video, frame_counts, fps, sample_frame = load.video(
    "/Users/troydo42/Desktop/TorchVision/INFER_VIDS/Nov30/00_01.mp4", 
    -90, 
    30
)

rect_sample = draw.fixed_rectangle_label(
    image=sample_frame, 
    anchor_point=[sample_frame.width/2, 100], 
    label_text="Lottee Mart",
    label_size=30, 
    label_font="/Users/troydo42/Desktop/Body_Matrixes/Roboto-Bold.ttf", 
    label_color ="#FFFFFF",
    background_color = "#11114A"
)

rect_sample

Connecting_Line

from body_matrix import draw
from body_matrix import load

video, frame_counts, fps, sample_frame = load.video(
    "/Users/troydo42/Desktop/TorchVision/INFER_VIDS/Nov30/00_01.mp4", 
    -90, 
    30
)

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(
    [
        'left_shoulder','right_shoulder',
        'left_hip','right_hip', 
        'left_wrist','right_wrist',
        'left_ankle', 'right_ankle'
    ], 
    keypoints
)

new_sample = draw.connecting_line(
    image=rect_sample, 
    pointA=selected_kps['right_shoulder'],
    pointB=selected_kps['left_hip'],
    line_color="white", 
    line_width=12
)

new_sample

Floating Label

from body_matrix import load
from body_matrix import measure 
from body_matrix import draw

video, frame_counts, fps, sample_frame = load.video(
    "/Users/troydo42/Desktop/TorchVision/INFER_VIDS/Nov30/00_01.mp4", 
    -90, 
    30
)

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(
    [
        'left_shoulder','right_shoulder',
        'left_hip','right_hip', 
        'left_wrist','right_wrist',
        'left_ankle', 'right_ankle'
    ], 
    keypoints
)

middle_hip = measure.find_middle_point(
    selected_kps['left_hip'], 
    selected_kps['right_hip']
)

middle_shoulder = measure.find_middle_point(
    selected_kps['left_shoulder'], 
    selected_kps['right_shoulder']
)

middle_back = measure.find_middle_point(
    middle_shoulder, 
    middle_hip
)

float_sample = sample_frame
for key, value in selected_kps.items():
    print(key, value)
    float_sample = draw.floating_rectangle_label(
        image=final_sample, 
        longitude_coordinate=middle_back[0],
        point=value, 
        label_text=key, 
        label_size=16, 
        label_color="#ffffff", 
        label_font="/Users/troydo42/Desktop/Body_Matrixes/Roboto-Bold.ttf",
        background_color="#11114A"
    )    

float_sample

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

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