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import cv2
import numpy as np
roi_bad_pixel_number = 500 # the number of pixel where 3 values are zero in RGB channel on ROI image
roi_aspect_ratio_threshold = 200 # the passable aspect ratio threshold of ROI image
cupped_pose_threshold = 3 # this value determines if hand is cupped or not
frame_margin = 300 # the extra marine size of the frame inputted into Google Mediapipe Graph (this value must be a
# multiple of two)
roi_size_threshold = 0.23
def mat_to_bytes(mat):
"""
Convert cv::Mat image data (NumPy array in Python) to raw bytes.
"""
# Encode cv::Mat as PNG bytes
is_success, buffer = cv2.imencode(".png", mat)
if not is_success:
raise ValueError("Failed to encode cv::Mat image")
return buffer.tobytes()
def img_crop(img_original, x2, x1, y2, y1, label):
h, w, _ = img_original.shape
img = np.zeros((h + 20, w + 20, 3), np.uint8)
img[10:-10, 10:-10, :] = img_original
if label == "Left":
v1 = np.array([x2, y2])
v2 = np.array([x1, y1])
else:
v2 = np.array([x2, y2])
v1 = np.array([x1, y1])
theta = np.arctan2((v2 - v1)[1], (v2 - v1)[0]) * 180 / np.pi
R = cv2.getRotationMatrix2D(tuple([int(v2[0]), int(v2[1])]), theta, 1)
v1 = (R[:, :2] @ v1 + R[:, -1]).astype(int)
v2 = (R[:, :2] @ v2 + R[:, -1]).astype(int)
img_r = cv2.warpAffine(img, R, (w, h))
if 1:
ux = int(v1[0] - (v2 - v1)[0] * 0.05)
uy = int(v1[1] + (v2 - v1)[0] * 0.05)
lx = int(v2[0] + (v2 - v1)[0] * 0.05)
ly = int(v2[1] + (v2 - v1)[0] * 1)
else:
ux = int(v1[0] - (v2 - v1)[0] * 0.1)
uy = int(v1[1] + (v2 - v1)[0] * 0.1)
lx = int(v2[0] + (v2 - v1)[0] * 0.1)
ly = int(v2[1] + (v2 - v1)[0] * 1.2)
# delta_y is movement value in y ward
delta_y = (ly - uy) * 0.15
ly = int(ly - delta_y)
uy = int(uy - delta_y)
delta_x = (lx - ux) * 0.01
lx = int(lx + delta_x)
ux = int(ux + delta_x)
if label == "Right":
delta_x = (lx - ux) * 0.05
lx = int(lx + delta_x)
ux = int(ux + delta_x)
# roi = img_r
roi = img_r[uy:ly, ux:lx]
if roi.shape[0] == 0 or roi.shape[1] == 0:
print("error 1")
return None, 3
if abs(roi.shape[0] - roi.shape[1]) > roi_aspect_ratio_threshold:
print("error 2", abs(roi.shape[0] - roi.shape[1]))
return None, 4
if roi.shape[1] / w < roi_size_threshold:
print("error 3", roi.shape[1] / w)
return None, 7
n_zeros = np.count_nonzero(roi == 0)
# if n_zeros > roi_bad_pixel_number:
# print("error 4", n_zeros)
# return None, 5
return roi, 0
def get_roi(img, hand_type, x1, y1, x2, y2):
if hand_type == 0:
label = "Left"
else:
label = "Right"
roi, _ = img_crop(img, x1, x2, y1, y2, label)
cv2.imwrite('test.jpg', roi)
return roi
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