landmarks.py

@@ -1,7 +1,5 @@
 import warnings
 import os
-from tabulate import tabulate
-import math
 
 warnings.filterwarnings("ignore",
                         category=UserWarning,
@@ -10,6 +8,7 @@ warnings.filterwarnings("ignore",
 os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
 
 import argparse
+import math
 import cv2
 from mediapipe.python.solutions import pose
 import logging
@@ -101,17 +100,17 @@ class Landmarker:
 
     def run(self):
 
-        logging.warning("person's height: %s", self.person_height)
+        logging.warning("person's height: ", self.person_height)
 
-        logging.warning("person's pixel height: %s", self.pixel_height)
+        logging.warning("person's pixel height: ", self.pixel_height)
 
-        front_results, side_results = self.process_images()
+        front_results = self.process_images()
 
         self.get_center_top_point(front_results)
 
         self.calculate_distance_betn_landmarks(front_results)
 
-        self.output()
+        self.print_distance()
 
         self.display_images()
 
@@ -138,12 +137,23 @@ class Landmarker:
                 side_results.pose_landmarks,  # type: ignore# type: ignore
                 self.landmarks_indices,
             )
-        return front_results, side_results
+        return front_results
+
+    def calculate_distance(self, landmark1, landmark2):
+        l1 = landmark1.x, landmark1.y
+        l1 = int(l1[0] * self.resized_height), int(l1[1] * self.resized_width)
+        x1, y1 = l1
+        l2 = landmark2.x, landmark2.y
+        l2 = int(l2[0] * self.resized_height), int(l2[1] * self.resized_width)
+        x2, y2 = l2
+        pixel_distance = self.euclidean_distance(x1, x2, y1, y2)
+        real_distance = pixel_distance * self.pixel_to_metric_ratio()
+        return real_distance
 
     def pixel_to_metric_ratio(self):
         self.pixel_height = self.pixel_distance * 2
         pixel_to_metric_ratio = self.person_height / self.pixel_height
-        logging.warning("pixel_to_metric_ratio %s", pixel_to_metric_ratio)
+        logging.warning("pixel_to_metric_ratio", pixel_to_metric_ratio)
         return pixel_to_metric_ratio
 
     def draw_landmarks(self, image, landmarks, indices):
@@ -156,59 +166,53 @@ class Landmarker:
     def circle(self, image, cx, cy):
         return cv2.circle(image, (cx, cy), 2, (255, 0, 0), -1)
 
-    def output(self):
+    def print_distance(self):
-        table = [[
+        print(
-            "shoulder_elbow",
+            "Distance between left shoulder and left elbow:",
-        ], [
+            self.distance_left_hand_up,
-            "elbow_wrist",
+        )
-        ], [
-            "hip_knee",
-        ], [
-            "knee_ankel",
-        ]]
-        output = tabulate(table,
-                          headers=["measurement", "value"],
-                          tablefmt="plain")
-        print(output)
 
-    def get_landmarks(self):
+        print(
-        self.landmarks_to_calculate = [[
+            "Distance between left elbow and left wrist:",
-            pose.PoseLandmark.LEFT_SHOULDER, pose.PoseLandmark.LEFT_ELBOW,
+            self.distance_left_hand_down,
-            pose.PoseLandmark.LEFT_WRIST
+        )
-        ],
-                                       [
-                                           pose.PoseLandmark.LEFT_HIP,
-                                           pose.PoseLandmark.LEFT_KNEE,
-                                           pose.PoseLandmark.LEFT_ANKLE
-                                       ]]
-        #return
 
-    def calculate_distance_betn_landmarks(self, front_results, landmarks=[]):
+        print("Distance between left hip and left knee:",
+              self.distance_left_leg_up)
+
+        print("Distance between left knee and left ankle:",
+              self.distance_left_leg_down)
+
+        print("Distance between center and top point:", self.distance)
+
+        print("Height of person:", self.distance * 2)
+
+    def calculate_distance_betn_landmarks(self, front_results):
         if not front_results.pose_landmarks:
             return
 
         landmarks = front_results.pose_landmarks.landmark
-        landmarks_to_calculate = [
-            pose.PoseLandmark.LEFT_SHOULDER, pose.PoseLandmark.LEFT_ELBOW,
-            pose.PoseLandmark.LEFT_WRIST
-        ]
-        #landmarks_to_calculate = self.get_landmarks();
-        for l in landmarks_to_calculate:
-            #l1 = int(l1[0] * self.resized_height), int(l1[1] * self.resized_width)
-            real_distance = 0
-            for idx, l in enumerate(landmarks_to_calculate):
-                if idx < len(landmarks_to_calculate) - 1:
-                    _current = landmarks[l.value]
-                    _nextl = landmarks_to_calculate[idx + 1]
-                    _next = landmarks[_nextl.value]
-                    pixel_distance = self.euclidean_distance(
-                        _current.x, _current.y, _next.x, _next.y)
-                    print(pixel_distance)
-                    real_distance += pixel_distance * self.pixel_to_metric_ratio(
-                    )
-            print(real_distance)
 
-    def euclidean_distance(self, x1, y1, x2, y2):
+        shoulder_left = landmarks[pose.PoseLandmark.LEFT_SHOULDER.value]
+        elbow_left = landmarks[pose.PoseLandmark.LEFT_ELBOW.value]
+        wrist_left = landmarks[pose.PoseLandmark.LEFT_WRIST.value]
+        hip_left = landmarks[pose.PoseLandmark.LEFT_HIP.value]
+        knee_left = landmarks[pose.PoseLandmark.LEFT_KNEE.value]
+        ankle_left = landmarks[pose.PoseLandmark.LEFT_ANKLE.value]
+
+        self.distance_left_hand_up = self.calculate_distance(
+            shoulder_left, elbow_left)
+
+        self.distance_left_hand_down = self.calculate_distance(
+            elbow_left, wrist_left)
+
+        self.distance_left_leg_up = self.calculate_distance(
+            hip_left, knee_left)
+
+        self.distance_left_leg_down = self.calculate_distance(
+            knee_left, ankle_left)
+
+    def euclidean_distance(self, x1, x2, y1, y2):
         distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
         return distance
 
@@ -239,8 +243,8 @@ class Landmarker:
             xt, yt = self.topmost_point
             self.circle(self.side_image_keypoints, xt, yt)
 
-            logging.warning("xt: %s", xt)
+            logging.warning("xt: ", xt)
-            logging.warning("yt: %s", yt)
+            logging.warning(f"yt: ")
         xc, yc = None, None
         landmarks = side_results.pose_landmarks.landmark
 
@@ -256,14 +260,13 @@ class Landmarker:
                 int(center_point[1] * self.side_image_resized.shape[0]),
             )
             xc, yc = center_point
-            logging.warning("xc: %s", xc)
+            logging.warning("xc: ", xc)
-            logging.warning("yc: %s", yc)
+            logging.warning(f"yc: {yc}")
             self.circle(self.side_image_keypoints, xc, yc)
 
             self.pixel_distance = self.euclidean_distance(xc, xt, yc, yt)
-            self.distance = (self.euclidean_distance(xc, xt, yc, yt) *
+            self.distance = self.euclidean_distance(
-                             self.pixel_to_metric_ratio())
+                xc, xt, yc, yt) * self.pixel_to_metric_ratio()
-
         return self.distance
 
 

temp.py

@@ -1,61 +1,40 @@
-import warnings
-import os
-from tabulate import tabulate
-
-warnings.filterwarnings("ignore",
-                        category=UserWarning,
-                        module="google.protobuf")
-
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
-
 import argparse
 import math
 import cv2
-from mediapipe.python.solutions import pose
+import mediapipe as mp
-import logging
 
 
 class Landmarker:
-    resized_height = 256
-    resized_width = 300
 
     def __init__(self) -> None:
         args = self.parse_args()
-        if args.front_image is None:
+        if args.front_image == None:
-            raise Exception("Front image needs to be passed")
+            raise Exception("front image needs to be passed")
-        if args.side_image is None:
+        if args.side_image == None:
-            raise Exception("Side image needs to be passed")
+            raise Exception("side image needs to be passed")
 
         self.front_image = cv2.imread(args.front_image)
         self.side_image = cv2.imread(args.side_image)
 
-        self.front_image_resized = cv2.resize(
+        mp_pose = mp.solutions.pose
-            self.front_image, (self.resized_height, self.resized_width))
+        self.pose = mp_pose.Pose(
-        self.side_image_resized = cv2.resize(
-            self.side_image, (self.resized_height, self.resized_width))
-
-        self.person_height = args.person_height
-        self.pixel_height = args.pixel_height
-
-        self.pose = pose.Pose(
             static_image_mode=True,
             min_detection_confidence=args.pose_detection_confidence,
             min_tracking_confidence=args.pose_tracking_confidence,
         )
 
+        self.resized_height = 256
+        self.resized_width = int(
+            self.front_image.shape[1] *
+            (self.resized_height / self.front_image.shape[0]))
+
+        self.front_image_resized = cv2.resize(
+            self.front_image, (self.resized_width, self.resized_height))
+        self.side_image_resized = cv2.resize(
+            self.side_image, (self.resized_width, self.resized_height))
+
         self.landmarks_indices = [
-            pose.PoseLandmark.LEFT_SHOULDER.value,
+            11, 12, 13, 14, 15, 16, 23, 24, 25, 26, 27, 28
-            pose.PoseLandmark.RIGHT_SHOULDER.value,
-            pose.PoseLandmark.LEFT_ELBOW.value,
-            pose.PoseLandmark.RIGHT_ELBOW.value,
-            pose.PoseLandmark.LEFT_WRIST.value,
-            pose.PoseLandmark.RIGHT_WRIST.value,
-            pose.PoseLandmark.LEFT_HIP.value,
-            pose.PoseLandmark.RIGHT_HIP.value,
-            pose.PoseLandmark.LEFT_KNEE.value,
-            pose.PoseLandmark.RIGHT_KNEE.value,
-            pose.PoseLandmark.LEFT_ANKLE.value,
-            pose.PoseLandmark.RIGHT_ANKLE.value,
         ]
 
     def parse_args(self):
@@ -68,52 +47,19 @@ class Landmarker:
                             dest="side_image",
                             type=str,
                             help="Side image")
-        parser.add_argument(
+        parser.add_argument("--pose_detection_confidence",
-            "--pose_detection_confidence",
+                            deafult=0.5,
-            dest="pose_detection_confidence",
+                            dest="Pose detection confidence",
-            default=0.5,
+                            type=float,
-            type=float,
+                            help="Confidence score for pose detection")
-            help="Confidence score for pose detection",
+        parser.add_argument("--pose_tracking_confidence",
-        )
+                            default=0.5,
-        parser.add_argument(
+                            dest="Pose detection tracking",
-            "--pose_tracking_confidence",
+                            type=float,
-            dest="pose_tracking_confidence",
+                            help="Confidence score for pose tracking")
-            default=0.5,
-            type=float,
-            help="Confidence score for pose tracking",
-        )
-        parser.add_argument(
-            "--person_height",
-            default=153,
-            dest="person_height",
-            type=int,
-            help="Person height",
-        )
-        parser.add_argument(
-            "--pixel_height",
-            dest="pixel_height",
-            type=int,
-            help="Pixel height of person",
-        )
         return parser.parse_args()
 
     def run(self):
-        logging.warning("Person's height: %s", self.person_height)
-        logging.warning("Person's pixel height: %s", self.pixel_height)
-
-        front_results, side_results = self.process_images()
-
-        self.get_center_top_point(front_results)
-
-        self.calculate_distance_for_landmarks(front_results)
-
-        self.output()
-
-        self.display_images()
-
-        self.pose.close()
-
-    def process_images(self):
         front_results = self.pose.process(
             cv2.cvtColor(self.front_image_resized, cv2.COLOR_BGR2RGB))
         side_results = self.pose.process(
@@ -122,116 +68,143 @@ class Landmarker:
         self.side_image_keypoints = self.side_image_resized.copy()
         self.front_image_keypoints = self.front_image_resized.copy()
 
-        if front_results.pose_landmarks:  # type: ignore
+        if front_results.pose_landmarks:
             self.draw_landmarks(
                 self.front_image_keypoints,
-                front_results.pose_landmarks,  # type: ignore
+                front_results.pose_landmarks,
                 self.landmarks_indices,
             )
-        if side_results.pose_landmarks:  # type: ignore
+        if side_results.pose_landmarks:
             self.draw_landmarks(
                 self.side_image_keypoints,
-                side_results.pose_landmarks,  # type: ignore
+                side_results.pose_landmarks,
                 self.landmarks_indices,
             )
-        return front_results, side_results
 
-    def calculate_distance_for_landmarks(self, landmarks):
+        self.get_center_top_point(front_results)
-        real_distance = 0
+
-        if len(landmarks) > 1:
+        self.print_distance(front_results)
-            for i in range(0, len(landmarks) - 1):
+
-                _current = landmarks[self.landmarks_indices[i]]
+        self.display_images()
-                _next = landmarks[self.landmarks_indices[i + 1]]
+
-                pixel_distance = self.euclidean_distance(
+        self.pose.close()
-                    _current.x, _current.y, _next.x, _next.y)
+
-                real_distance += pixel_distance * self.pixel_to_metric_ratio()
+    def calculate_distance(self, landmark1, landmark2):
-        logging.warning("Total real distance: %s", real_distance)
+        l1 = landmark1.x, landmark1.y
+        l1 = int(l1[0] * self.resized_width), int(l1[1] * self.resized_height)
+        x1, y1 = l1
+        l2 = landmark2.x, landmark2.y
+        l2 = int(l2[0] * self.resized_width), int(l2[1] * self.resized_height)
+        x2, y2 = l2
+        pixel_distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
+        real_distance = pixel_distance * self.pixel_to_metric_ratio()
         return real_distance
 
     def pixel_to_metric_ratio(self):
-        self.pixel_height = self.pixel_distance * 2
+        height_person = 153
-        pixel_to_metric_ratio = self.person_height / self.pixel_height
+        pixel_distance_height = 255
-        logging.warning("Pixel to metric ratio: %s", pixel_to_metric_ratio)
+        pixel_to_metric_ratio = height_person / pixel_distance_height
+        pixel_to_metric_ratio = round(pixel_to_metric_ratio)
         return pixel_to_metric_ratio
 
     def draw_landmarks(self, image, landmarks, indices):
         for idx in indices:
-            landmark = landmarks[idx]
+            landmark = landmarks.landmark[idx]
             h, w, _ = image.shape
             cx, cy = int(landmark.x * w), int(landmark.y * h)
-            self.circle(image, cx, cy)
+            cv2.circle(image, (cx, cy), 2, (255, 0, 0), -1)
 
-    def circle(self, image, cx, cy):
+    def print_distance(self, front_results):
-        return cv2.circle(image, (cx, cy), 2, (255, 0, 0), -1)
+        if not front_results.pose_landmarks:
+            return
 
-    def output(self):
+        landmarks = front_results.pose_landmarks.landmark
-        table = [["shoulder", 1], ["neck", 2]]
-        output = tabulate(table,
-                          headers=["Measurement", "Value"],
-                          tablefmt="plain")
-        print(output)
 
-    def euclidean_distance(self, x1, y1, x2, y2):
+        shoulder_left = landmarks[11]
-        distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
+        elbow_left = landmarks[13]
-        return distance
+        wrist_left = landmarks[15]
+        hip_left = landmarks[23]
+        knee_left = landmarks[25]
+        ankle_left = landmarks[27]
 
-    def destroy(self):
+        distance_left_hand_up = self.calculate_distance(
+            shoulder_left, elbow_left)
+        print("Distance between left shoulder and left elbow:",
+              distance_left_hand_up)
+
+        distance_left_hand_down = self.calculate_distance(
+            elbow_left, wrist_left)
+        print("Distance between left elbow and left wrist:",
+              distance_left_hand_down)
+
+        distance_left_leg_up = self.calculate_distance(hip_left, knee_left)
+        print("Distance between left hip and left knee:", distance_left_leg_up)
+
+        distance_left_leg_down = self.calculate_distance(knee_left, ankle_left)
+        print("Distance between left knee and left ankle:",
+              distance_left_leg_down)
+
+    def destroy():
         cv2.destroyAllWindows()
 
     def display_images(self):
         cv2.imshow("front_image_keypoints", self.front_image_keypoints)
         cv2.imshow("side_image_keypoints", self.side_image_keypoints)
-        cv2.imshow("edges", self.edges)
-        cv2.waitKey(0)
 
-    def get_center_top_point(self, side_results):
+    def get_center_top_point(self, front_results):
-        gray_image = cv2.cvtColor(self.side_image_keypoints,
+        gray_image = cv2.cvtColor(self.front_image_keypoints,
                                   cv2.COLOR_BGR2GRAY)
         blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
-        roi = blurred_image[0:int(self.side_image_resized.shape[0] / 2), :]
+        roi = blurred_image[0:int(self.resized_height / 2), :]
-        self.edges = cv2.Canny(roi, 50, 150)
+        edges = cv2.Canny(roi, 50, 150)
-        contours, _ = cv2.findContours(self.edges, cv2.RETR_EXTERNAL,
+        contours, hierarchy = cv2.findContours(edges, cv2.RETR_TREE,
-                                       cv2.CHAIN_APPROX_SIMPLE)
+                                               cv2.CHAIN_APPROX_SIMPLE)
-        xt, yt = None, None
+
         self.topmost_point = None
 
-        if contours:
+        outermost_contours = [
-            largest_contour = max(contours, key=cv2.contourArea)
+            contours[i] for i in range(len(contours))
+            if hierarchy[0][i][3] == -1
+        ]
+        if outermost_contours:
+            largest_contour = max(outermost_contours, key=cv2.contourArea)
             self.topmost_point = tuple(
                 largest_contour[largest_contour[:, :, 1].argmin()][0])
-            xt, yt = self.topmost_point
+            # topmost_point = (int(topmost_point[0]*self.resized_width), int(topmost_point[1] * self.resized_height))
-            self.circle(self.side_image_keypoints, xt, yt)
+            topmost_point = (self.topmost_point[0], self.topmost_point[1])
+            cv2.circle(self.front_image_keypoints, topmost_point, 2,
+                       (255, 0, 0), -1)
 
-            logging.warning("xt: %s", xt)
+        self.left_hip_idx = 23
-            logging.warning("yt: %s", yt)
+        self.right_hip_idx = 24
-        xc, yc = None, None
-        landmarks = side_results.pose_landmarks.landmark
 
-        if side_results.pose_landmarks:  # type: ignore
+        self.center_point = None
-            left_hip = landmarks[pose.PoseLandmark.LEFT_HIP.value]
+
-            right_hip = landmarks[pose.PoseLandmark.RIGHT_HIP.value]
+        if front_results.pose_landmarks:
+            left_hip = front_results.pose_landmarks.landmark[self.left_hip_idx]
+            right_hip = front_results.pose_landmarks.landmark[
+                self.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] * self.side_image_resized.shape[1]),
+                int(center_point[0] * self.resized_width),
-                int(center_point[1] * self.side_image_resized.shape[0]),
+                int(center_point[1] * self.resized_height),
             )
+            cv2.circle(self.front_image_keypoints, center_point, 2,
+                       (255, 0, 0), -1)
             xc, yc = center_point
-            logging.warning("xc: %s", xc)
+            xt, yt = topmost_point
-            logging.warning("yc: %s", yc)
+            distance = math.sqrt((xc - xt)**2 + (yc - yt)**2)
-            self.circle(self.side_image_keypoints, xc, yc)
+            #return distance
-
+            return center_point
-            self.pixel_distance = self.euclidean_distance(xc, xt, yc, yt)
-            self.distance = self.pixel_distance * self.pixel_to_metric_ratio()
-        return self.distance
 
 
 l = Landmarker()
 try:
     l.run()
-except Exception as e:
+except:
-    print(f"Error: {e}")
+    pass
 finally:
     l.destroy()

trial.py

@@ -1,38 +0,0 @@
-import cv2
-import mediapipe as mp
-
-
-def detect_points(image_path):
-    image = cv2.imread(image_path)
-    target_width = 256
-    target_height = int(image.shape[0] * target_width / image.shape[1])
-
-    image = cv2.resize(image, (target_width, target_height))
-
-    mp_pose = mp.solutions.pose  #type: ignore
-
-    pose = mp_pose.Pose(static_image_mode=True, min_detection_confidence=0.5)
-
-    image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
-
-    results = pose.process(image_rgb)
-
-    if results.pose_landmarks:
-        for landmark in results.pose_landmarks.landmark:
-            cv2.circle(
-                image,
-                (int(landmark.x * image.shape[1]),
-                 int(landmark.y * image.shape[0])),
-                5,
-                (0, 255, 0),
-                -1,
-            )
-
-    cv2.imshow("Image", image)
-    cv2.waitKey(0)
-    cv2.destroyAllWindows()
-
-
-image_path = "/home/aparna/projects/aubome/assets/aparna_side.jpg"
-
-detect_points(image_path)