refactor: add missing interpolators for the string in logging
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7ffdaaca26
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72
landmarks.py
72
landmarks.py
@ -1,9 +1,7 @@
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import warnings
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import os
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warnings.filterwarnings("ignore",
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category=UserWarning,
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module="google.protobuf")
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warnings.filterwarnings("ignore", category=UserWarning, module="google.protobuf")
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os.environ["TF_CPP_MIN_LOG_LEVEL"] = "3"
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@ -30,9 +28,11 @@ class Landmarker:
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self.side_image = cv2.imread(args.side_image)
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self.front_image_resized = cv2.resize(
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self.front_image, (self.resized_height, self.resized_width))
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self.front_image, (self.resized_height, self.resized_width)
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)
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self.side_image_resized = cv2.resize(
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self.side_image, (self.resized_height, self.resized_width))
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self.side_image, (self.resized_height, self.resized_width)
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)
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self.person_height = args.person_height
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self.pixel_height = args.pixel_height
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@ -60,14 +60,8 @@ class Landmarker:
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def parse_args(self):
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parser = argparse.ArgumentParser()
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parser.add_argument("--front",
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dest="front_image",
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type=str,
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help="Front image")
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parser.add_argument("--side",
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dest="side_image",
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type=str,
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help="Side image")
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parser.add_argument("--front", dest="front_image", type=str, help="Front image")
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parser.add_argument("--side", dest="side_image", type=str, help="Side image")
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parser.add_argument(
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"--pose_detection_confidence",
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dest="pose_detection_confidence",
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@ -100,9 +94,9 @@ class Landmarker:
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def run(self):
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logging.warning("person's height: ", self.person_height)
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logging.warning("person's height: %s", self.person_height)
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logging.warning("person's pixel height: ", self.pixel_height)
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logging.warning("person's pixel height: %s", self.pixel_height)
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front_results = self.process_images()
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@ -118,9 +112,11 @@ class Landmarker:
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def process_images(self):
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front_results = self.pose.process(
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cv2.cvtColor(self.front_image_resized, cv2.COLOR_BGR2RGB))
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cv2.cvtColor(self.front_image_resized, cv2.COLOR_BGR2RGB)
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)
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side_results = self.pose.process(
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cv2.cvtColor(self.side_image_resized, cv2.COLOR_BGR2RGB))
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cv2.cvtColor(self.side_image_resized, cv2.COLOR_BGR2RGB)
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)
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self.side_image_keypoints = self.side_image_resized.copy()
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self.front_image_keypoints = self.front_image_resized.copy()
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@ -153,7 +149,7 @@ class Landmarker:
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def pixel_to_metric_ratio(self):
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self.pixel_height = self.pixel_distance * 2
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pixel_to_metric_ratio = self.person_height / self.pixel_height
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logging.warning("pixel_to_metric_ratio", pixel_to_metric_ratio)
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logging.warning("pixel_to_metric_ratio %s", pixel_to_metric_ratio)
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return pixel_to_metric_ratio
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def draw_landmarks(self, image, landmarks, indices):
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@ -200,20 +196,16 @@ class Landmarker:
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knee_left = landmarks[pose.PoseLandmark.LEFT_KNEE.value]
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ankle_left = landmarks[pose.PoseLandmark.LEFT_ANKLE.value]
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self.distance_left_hand_up = self.calculate_distance(
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shoulder_left, elbow_left)
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self.distance_left_hand_up = self.calculate_distance(shoulder_left, elbow_left)
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self.distance_left_hand_down = self.calculate_distance(
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elbow_left, wrist_left)
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self.distance_left_hand_down = self.calculate_distance(elbow_left, wrist_left)
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self.distance_left_leg_up = self.calculate_distance(
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hip_left, knee_left)
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self.distance_left_leg_up = self.calculate_distance(hip_left, knee_left)
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self.distance_left_leg_down = self.calculate_distance(
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knee_left, ankle_left)
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self.distance_left_leg_down = self.calculate_distance(knee_left, ankle_left)
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def euclidean_distance(self, x1, x2, y1, y2):
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distance = math.sqrt((x2 - x1)**2 + (y2 - y1)**2)
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distance = math.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2)
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return distance
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def destroy(self):
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@ -226,25 +218,26 @@ class Landmarker:
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cv2.waitKey(0)
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def get_center_top_point(self, side_results):
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gray_image = cv2.cvtColor(self.side_image_keypoints,
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cv2.COLOR_BGR2GRAY)
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gray_image = cv2.cvtColor(self.side_image_keypoints, cv2.COLOR_BGR2GRAY)
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blurred_image = cv2.GaussianBlur(gray_image, (5, 5), 0)
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roi = blurred_image[0:int(self.side_image_resized.shape[0] / 2), :]
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roi = blurred_image[0 : int(self.side_image_resized.shape[0] / 2), :]
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self.edges = cv2.Canny(roi, 50, 150)
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contours, _ = cv2.findContours(self.edges, cv2.RETR_EXTERNAL,
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cv2.CHAIN_APPROX_SIMPLE)
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contours, _ = cv2.findContours(
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self.edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE
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)
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xt, yt = None, None
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self.topmost_point = None
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if contours:
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largest_contour = max(contours, key=cv2.contourArea)
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self.topmost_point = tuple(
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largest_contour[largest_contour[:, :, 1].argmin()][0])
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largest_contour[largest_contour[:, :, 1].argmin()][0]
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)
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xt, yt = self.topmost_point
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self.circle(self.side_image_keypoints, xt, yt)
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logging.warning("xt: ", xt)
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logging.warning(f"yt: ")
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logging.warning("xt: %s", xt)
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logging.warning("yt: %s", yt)
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xc, yc = None, None
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landmarks = side_results.pose_landmarks.landmark
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@ -260,13 +253,14 @@ class Landmarker:
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int(center_point[1] * self.side_image_resized.shape[0]),
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)
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xc, yc = center_point
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logging.warning("xc: ", xc)
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logging.warning(f"yc: {yc}")
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logging.warning("xc: %s", xc)
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logging.warning("yc: %s", yc)
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self.circle(self.side_image_keypoints, xc, yc)
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self.pixel_distance = self.euclidean_distance(xc, xt, yc, yt)
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self.distance = self.euclidean_distance(
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xc, xt, yc, yt) * self.pixel_to_metric_ratio()
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self.distance = (
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self.euclidean_distance(xc, xt, yc, yt) * self.pixel_to_metric_ratio()
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)
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return self.distance
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