Mediapiple - False positives

I was looking to experiment existence of face, full body but mediapipe does not seem to give accurate results

Example Image #1 (No Foot / Partial Picture)

Detected Coordinates are

Example Image (No Face Present)

Detected Coordinates are

Seems to need to go with custom models :)

Nothing is fool proof

	#https://github.com/google/mediapipe/blob/master/mediapipe/python/solutions/pose_connections.py
	#https://github.com/google/mediapipe/blob/master/mediapipe/python/solutions/pose.py
	import cv2
	import mediapipe as mp
	import numpy as np
	mp_drawing = mp.solutions.drawing_utils
	mp_drawing_styles = mp.solutions.drawing_styles
	mp_pose = mp.solutions.pose
	# For static images:
	IMAGE_FILES = ['Sample2.jpg']
	BG_COLOR = (192, 192, 192) # gray
	with mp_pose.Pose(
	static_image_mode=True,
	model_complexity=2,
	enable_segmentation=True,
	min_detection_confidence=0.5) as pose:
	for idx, file in enumerate(IMAGE_FILES):
	image = cv2.imread(file)
	image_height, image_width, _ = image.shape
	# Convert the BGR image to RGB before processing.
	results = pose.process(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
	if not results.pose_landmarks:
	continue
	print(
	f'LEFT_EYE coordinates: ('
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_EYE].x * image_width}, '
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_EYE].y * image_height})'
	)
	print(
	f'RIGHT_EYE coordinates: ('
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_EYE].x * image_width}, '
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_EYE].y * image_height})'
	)
	print(
	f'Nose coordinates: ('
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].x * image_width}, '
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.NOSE].y * image_height})'
	)
	print(
	f'LEFT_FOOT_INDEX coordinates: ('
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_FOOT_INDEX].x * image_width}, '
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_FOOT_INDEX].y * image_height})'
	)
	print(
	f'RIGHT_FOOT_INDEX coordinates: ('
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX].x * image_width}, '
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_FOOT_INDEX].y * image_height})'
	)
	print(
	f'LEFT_SHOULDER coordinates: ('
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].x * image_width}, '
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.LEFT_SHOULDER].y * image_height})'
	)
	print(
	f'RIGHT_SHOULDER coordinates: ('
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].x * image_width}, '
	f'{results.pose_landmarks.landmark[mp_pose.PoseLandmark.RIGHT_SHOULDER].y * image_height})'
	)
	annotated_image = image.copy()
	# Draw segmentation on the image.
	# To improve segmentation around boundaries, consider applying a joint
	# bilateral filter to "results.segmentation_mask" with "image".
	condition = np.stack((results.segmentation_mask,) * 3, axis=-1) > 0.1
	bg_image = np.zeros(image.shape, dtype=np.uint8)
	bg_image[:] = BG_COLOR
	annotated_image = np.where(condition, annotated_image, bg_image)
	# Draw pose landmarks on the image.
	mp_drawing.draw_landmarks(
	annotated_image,
	results.pose_landmarks,
	mp_pose.POSE_CONNECTIONS,
	landmark_drawing_spec=mp_drawing_styles.get_default_pose_landmarks_style())
	cv2.imwrite('/tmp/annotated_image' + str(idx) + '.png', annotated_image)
	# Plot pose world landmarks.
	mp_drawing.plot_landmarks(
	results.pose_world_landmarks, mp_pose.POSE_CONNECTIONS)

view raw mediapipepose.py hosted with ❤ by GitHub

Keep Exploring!!!