aubome/landmarks.py

import math
import cv2
import mediapipe as mp
import matplotlib.pyplot as plt
import numpy as np

front_image = cv2.imread("/home/aparna/body_measurements_mp/assests/aparna.jpg")
side_image = cv2.imread(
    "/home/aparna/body_measurements_mp/body_measurement_using_cv/Images/final3.jpg"
)

resized_height = 256
resized_width = int(front_image.shape[1] * (resized_height / front_image.shape[0]))
front_image_resized = cv2.resize(front_image, (resized_width, resized_height))
side_image_resized = cv2.resize(side_image, (resized_width, resized_height))

front_image_gray = cv2.cvtColor(front_image_resized, cv2.COLOR_BGR2GRAY)
side_image_gray = cv2.cvtColor(side_image_resized, cv2.COLOR_BGR2GRAY)

front_image_normalized = front_image_gray.astype("float32") / 255.0
side_image_normalized = side_image_gray.astype("float32") / 255.0

cv2.imshow("front_img", front_image_normalized)
cv2.imshow("side_image", side_image_normalized)

mp_pose = mp.solutions.pose
pose = mp_pose.Pose(
    static_image_mode=True, min_detection_confidence=0.5, min_tracking_confidence=0.5
)

resized_height = 256
resized_width = int(front_image.shape[1] * (resized_height / front_image.shape[0]))
front_image_resized = cv2.resize(front_image, (resized_width, resized_height))

landmark_indices = [11, 12, 13, 14, 15, 16, 23, 24, 25, 26, 27, 28]

front_results = pose.process(cv2.cvtColor(front_image_resized, cv2.COLOR_BGR2RGB))
side_results = pose.process(cv2.cvtColor(side_image_resized, cv2.COLOR_BGR2RGB))

front_image_keypoints = front_image_resized.copy()
side_image_keypoints = side_image_resized.copy()


def calculate_distance(landmark1, landmark2, pixel_to_metric_ratio):
    l1 = landmark1.x, landmark1.y
    l1 = int(l1[0] * resized_width), int(l1[1] * resized_height)
    x1, y1 = l1
    l2 = landmark2.x, landmark2.y
    l2 = int(l2[0] * resized_width), int(l2[1] * resized_height)
    x2, y2 = l2
    print(
        "x1: ",
        landmark1.x,
        "y1: ",
        landmark1.y,
        "x2: ",
        landmark2.x,
        "y2: ",
        landmark2.y,
    )
    pixel_distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
    real_life_distance = pixel_distance * pixel_to_metric_ratio
    return real_life_distance


def draw_landmarks(image, landmarks, indices):
    for idx in indices:
        landmark = landmarks.landmark[idx]
        h, w, _ = image.shape
        cx, cy = int(landmark.x * w), int(landmark.y * h)
        cv2.circle(image, (cx, cy), 2, (255, 0, 0), -1)


if front_results.pose_landmarks:
    draw_landmarks(
        front_image_keypoints, front_results.pose_landmarks, landmark_indices
    )

if side_results.pose_landmarks:
    draw_landmarks(side_image_keypoints, side_results.pose_landmarks, landmark_indices)

h, w, _ = front_image_keypoints.shape
height_cm = 150
pixel_distance_height = 126.32 * 2
pixel_to_metric_ratio = height_cm / pixel_distance_height

if front_results.pose_landmarks:
    shoulder_left = front_results.pose_landmarks.landmark[11]
    elbow_left = front_results.pose_landmarks.landmark[13]
    distance_left_hand_up = calculate_distance(
        shoulder_left, elbow_left, pixel_to_metric_ratio
    )
    print("Distance between left shoulder and left elbow:", distance_left_hand_up)

    elbow_left = front_results.pose_landmarks.landmark[13]
    wrist_left = front_results.pose_landmarks.landmark[15]
    distance_left_hand_down = calculate_distance(
        elbow_left, wrist_left, pixel_to_metric_ratio
    )
    print("Distance between left elbow and left wrist:", distance_left_hand_down)

    hip_left = front_results.pose_landmarks.landmark[23]
    knee_left = front_results.pose_landmarks.landmark[25]
    distance_left_leg_up = calculate_distance(
        hip_left, knee_left, pixel_to_metric_ratio
    )
    print("Distance between left hip and left knee:", distance_left_leg_up)

    knee_left = front_results.pose_landmarks.landmark[25]
    ankle_left = front_results.pose_landmarks.landmark[27]
    distance_left_leg_down = calculate_distance(
        knee_left, ankle_left, pixel_to_metric_ratio
    )
    print("Distance between left knee and left ankle:", distance_left_leg_down)

gray_image = cv2.cvtColor(front_image_keypoints, cv2.COLOR_BGR2GRAY)
blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
roi = blurred_image[0 : int(resized_height / 2), :]
edges = cv2.Canny(roi, 50, 150)

contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)

outermost_contours = [
    contours[i] for i in range(len(contours)) if hierarchy[0][i][3] == -1
]

topmost_point = None
if outermost_contours:
    largest_contour = max(outermost_contours, key=cv2.contourArea)
    topmost_point = tuple(largest_contour[largest_contour[:, :, 1].argmin()][0])
    topmost_point = (topmost_point[0], topmost_point[1])
    cv2.circle(front_image_keypoints, topmost_point, 2, (255, 0, 0), -1)

left_hip_idx = 23
right_hip_idx = 24

center_point = None
if front_results.pose_landmarks:
    left_hip = front_results.pose_landmarks.landmark[left_hip_idx]
    right_hip = front_results.pose_landmarks.landmark[right_hip_idx]
    center_point = ((left_hip.x + right_hip.x) / 2, (left_hip.y + right_hip.y) / 2)
    center_point = (
        int(center_point[0] * resized_width),
        int(center_point[1] * resized_height),
    )
    cv2.circle(front_image_keypoints, center_point, 2, (255, 0, 0), -1)

print("Pixel distance for height:", pixel_distance_height)
print("Pixel-to-metric ratio:", pixel_to_metric_ratio)

cv2.imshow("front_img_keypoints", front_image_keypoints)
cv2.imshow("side_img_keypoints", side_image_keypoints)

cv2.waitKey(0)
cv2.destroyAllWindows()
pose.close()