chore: initial commit

landmarks.py

@@ -0,0 +1,156 @@
+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()