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# This file is part of OpenCV Zoo project.
# It is subject to the license terms in the LICENSE file found in the same directory.
#
# Copyright (C) 2021, Shenzhen Institute of Artificial Intelligence and Robotics for Society, all rights reserved.
# Third party copyrights are property of their respective owners.
import collections
import numpy as np
import cv2 as cv
import sys
class Compose:
def __init__(self, transforms=[]):
self.transforms = transforms
def __call__(self, img):
for t in self.transforms:
img = t(img)
if img is None:
break
return img
class Resize:
def __init__(self, size, interpolation=cv.INTER_LINEAR):
self.size = size
self.interpolation = interpolation
def __call__(self, img):
return cv.resize(img, self.size)
class CenterCrop:
def __init__(self, size):
self.size = size # w, h
def __call__(self, img):
h, w, _ = img.shape
ws = int(w / 2 - self.size[0] / 2)
hs = int(h / 2 - self.size[1] / 2)
return img[hs:hs+self.size[1], ws:ws+self.size[0], :]
class Normalize:
def __init__(self, mean=None, std=None):
self.mean = mean
self.std = std
def __call__(self, img):
img = img.astype("float32")
if self.mean is not None:
img[:, :, 0] = img[:, :, 0] - self.mean[0]
img[:, :, 1] = img[:, :, 1] - self.mean[1]
img[:, :, 2] = img[:, :, 2] - self.mean[2]
if self.std is not None:
img[:, :, 0] = img[:, :, 0] / self.std[0]
img[:, :, 1] = img[:, :, 1] / self.std[1]
img[:, :, 2] = img[:, :, 2] / self.std[2]
return img
class ColorConvert:
def __init__(self, ctype):
self.ctype = ctype
def __call__(self, img):
return cv.cvtColor(img, self.ctype)
class HandAlign:
def __init__(self, model):
self.model = model
sys.path.append('../../models/palm_detection_mediapipe')
from mp_palmdet import MPPalmDet
self.palm_detector = MPPalmDet(modelPath='../../models/palm_detection_mediapipe/palm_detection_mediapipe_2023feb.onnx', nmsThreshold=0.3, scoreThreshold=0.9)
def __call__(self, img):
return self.mp_handpose_align(img)
def mp_handpose_align(self, img):
palms = self.palm_detector.infer(img)
if len(palms) == 0:
return None
palm = palms[0]
palm_bbox = palm[0:4].reshape(2, 2)
palm_landmarks = palm[4:18].reshape(7, 2)
p1 = palm_landmarks[0]
p2 = palm_landmarks[2]
radians = np.pi / 2 - np.arctan2(-(p2[1] - p1[1]), p2[0] - p1[0])
radians = radians - 2 * np.pi * np.floor((radians + np.pi) / (2 * np.pi))
angle = np.rad2deg(radians)
# get bbox center
center_palm_bbox = np.sum(palm_bbox, axis=0) / 2
# get rotation matrix
rotation_matrix = cv.getRotationMatrix2D(center_palm_bbox, angle, 1.0)
# get rotated image
rotated_image = cv.warpAffine(img, rotation_matrix, (img.shape[1], img.shape[0]))
# get bounding boxes from rotated palm landmarks
homogeneous_coord = np.c_[palm_landmarks, np.ones(palm_landmarks.shape[0])]
rotated_palm_landmarks = np.array([
np.dot(homogeneous_coord, rotation_matrix[0]),
np.dot(homogeneous_coord, rotation_matrix[1])])
# get landmark bounding box
rotated_palm_bbox = np.array([
np.amin(rotated_palm_landmarks, axis=1),
np.amax(rotated_palm_landmarks, axis=1)]) # [top-left, bottom-right]
# shift bounding box
wh_rotated_palm_bbox = rotated_palm_bbox[1] - rotated_palm_bbox[0]
shift_vector = [0, -0.1] * wh_rotated_palm_bbox
rotated_palm_bbox = rotated_palm_bbox + shift_vector
# squarify bounding boxx
center_rotated_plam_bbox = np.sum(rotated_palm_bbox, axis=0) / 2
wh_rotated_palm_bbox = rotated_palm_bbox[1] - rotated_palm_bbox[0]
new_half_size = np.amax(wh_rotated_palm_bbox) / 2
rotated_palm_bbox = np.array([
center_rotated_plam_bbox - new_half_size,
center_rotated_plam_bbox + new_half_size])
# enlarge bounding box
center_rotated_plam_bbox = np.sum(rotated_palm_bbox, axis=0) / 2
wh_rotated_palm_bbox = rotated_palm_bbox[1] - rotated_palm_bbox[0]
new_half_size = wh_rotated_palm_bbox * 1.5
rotated_palm_bbox = np.array([
center_rotated_plam_bbox - new_half_size,
center_rotated_plam_bbox + new_half_size])
# Crop the rotated image by the bounding box
[[x1, y1], [x2, y2]] = rotated_palm_bbox.astype(np.int32)
diff = np.maximum([-x1, -y1, x2 - rotated_image.shape[1], y2 - rotated_image.shape[0]], 0)
[x1, y1, x2, y2] = [x1, y1, x2, y2] + diff
crop = rotated_image[y1:y2, x1:x2, :]
crop = cv.copyMakeBorder(crop, diff[1], diff[3], diff[0], diff[2], cv.BORDER_CONSTANT, value=(0, 0, 0))
return crop
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