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import math
import numpy as np
import matplotlib.pyplot as plt
import cv2
from tensorflow.keras import preprocessing
def euclidean_distance(point1, point2):
return math.sqrt((point2[0] - point1[0]) ** 2 + (point2[1] - point1[1]) ** 2)
def convert_to_real_measurements(pixel_measurement, pixel_height, real_height_cm):
height_ratio = real_height_cm / pixel_height
return pixel_measurement * height_ratio
def measure_body_sizes(side_colored_mask, front_colored_mask, sideposes, frontposes, real_height_cm, rainbow):
"""Measure various body sizes based on detected poses."""
measurements = []
for pose in frontposes:
# Assuming each `pose` is a dictionary with 'keypoints' that are already in the required format
keypoints = pose[0] # This should directly give us the dictionary
# Extract positions directly from keypoints
left_eye = keypoints[1].position
right_eye = keypoints[2].position
nose = keypoints[3].position
right_ear = keypoints[4].position
left_shoulder = keypoints[5].position
right_shoulder = keypoints[6].position
left_elbow = keypoints[7].position
right_elbow = keypoints[8].position
left_wrist = keypoints[9].position
right_wrist = keypoints[10].position
left_hip = keypoints[11].position
right_hip = keypoints[12].position
left_knee = keypoints[13].position
right_knee = keypoints[14].position
left_ankle = keypoints[15].position
right_ankle = keypoints[16].position
# Calculate pixel height (from the top of the head to the bottom of the ankle)
pixel_height = euclidean_distance((left_eye.x, left_eye.y), (left_ankle.x, left_ankle.y))
shoulder_width_cm = convert_to_real_measurements(
euclidean_distance((left_shoulder.x, left_shoulder.y),(right_shoulder.x, right_shoulder.y)),
pixel_height, real_height_cm
)
# arm_length_cm = convert_to_real_measurements(
# euclidean_distance((right_shoulder.x, right_shoulder.y), (right_elbow.x, right_elbow.y)),
# pixel_height, real_height_cm
# ) + convert_to_real_measurements(
# euclidean_distance((right_elbow.x, right_elbow.y), (right_wrist.x, right_wrist.y)),
# pixel_height, real_height_cm
# )
# leg_length_cm = convert_to_real_measurements(
# euclidean_distance((left_hip.x, left_hip.y), (left_knee.x, left_knee.y)),
# pixel_height, real_height_cm
# ) + convert_to_real_measurements(
# euclidean_distance((left_knee.x, left_knee.y), (left_ankle.x, left_ankle.y)),
# pixel_height, real_height_cm
# )
arm_length_cm = convert_to_real_measurements(
euclidean_distance((left_shoulder.x, left_shoulder.y), (left_wrist.x, left_wrist.y)),
pixel_height, real_height_cm
)
leg_length_cm = convert_to_real_measurements(
euclidean_distance((left_hip.x, left_hip.y), (left_ankle.x, right_ankle.y)),
pixel_height, real_height_cm
)
shoulder_to_waist_cm = convert_to_real_measurements(
euclidean_distance((left_shoulder.x, left_shoulder.y), (left_hip.x, left_hip.y)),
pixel_height, real_height_cm
)
# Calculate waist circumference using the ellipse circumference formula
a = euclidean_distance((left_hip.x, left_hip.y), (right_hip.x, right_hip.y)) / 2
# b = euclidean_distance((), ()) / 2
# Use Ramanujan's approximation for the circumference of an ellipse
# waist_circumference_px = math.pi * (3*(a + b) - math.sqrt((3*a + b)*(a + 3*b)))
waist_circumference_cm = 90 #convert_to_real_measurements(waist_circumference_px, pixel_height, real_height_cm)
# Convert pixel measurements to real measurements using the height ratio
measurements.append({
"shoulder_width_cm": shoulder_width_cm,
"leg_length_cm": leg_length_cm,
"arm_length_cm": arm_length_cm,
"shoulder_to_waist_cm": shoulder_to_waist_cm,
"height_cm": real_height_cm,
"waist_circumference_cm": waist_circumference_cm
})
return measurements
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