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July 17, 2024 06:28
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Human landmark based relative dept estimation using MiDAS
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| #!/usr/bin/python3 | |
| #Author : Sateesh Kalidas | |
| #Date : 13/July/2024 | |
| #Reference: https://medium.com/artificialis/swift-and-simple-calculate-object-distance-with-ease-in-just-few-lines-of-code-38889575bb12 | |
| #Purpose : Landmark based depth estimation | |
| import mediapipe as mp | |
| import cv2 | |
| import numpy as np | |
| mp_pose = mp.solutions.pose | |
| pose = mp_pose.Pose(static_image_mode = False) | |
| #Not sure | |
| alpha = 0.6 | |
| previous_depth = 0.0 | |
| #Filter the depth | |
| def apply_ema_filter(current_depth): | |
| global previous_depth | |
| filtered_depth = alpha * current_depth + (1 - alpha) * previous_depth | |
| previous_depth = filtered_depth | |
| return filtered_depth | |
| # Relative depth into m, this is like black magic | |
| def depth_to_distance(depth_value, depth_scale): | |
| return -1.0 / (depth_value * depth_scale) | |
| # Input camera feed | |
| cap = cv2.VideoCapture('distance.mp4') | |
| while cap.isOpened(): | |
| ret, frame = cap.read() | |
| img = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB) | |
| results = pose.process(img) | |
| if results.pose_landmarks is not None: | |
| landmarks = [ ] | |
| for landmark in results.pose_landmarks.landmark: | |
| landmarks.append((landmark.x, landmark.y, landmark.z)) | |
| nose_landmark = landmarks[mp_pose.PoseLandmark.NOSE.value] | |
| _,_,nose_z = nose_landmark | |
| img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) | |
| filter = apply_ema_filter(nose_z) | |
| distance = depth_to_distance(filter,1) | |
| cv2.putText(img, 'Depth units:' + str(np.format_float_positional(distance, precision=1)),(20,50),cv2.FONT_HERSHEY_SIMPLEX,1, (255,255,255),3) | |
| cv2.imshow('ImgWindow',img) | |
| if cv2.waitKey(1) & 0xFF == ord('q'): | |
| cap.release() | |
| cv2.destroyAllWindows() |
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