Python cv2.hconcat() Examples
The following are 8
code examples of cv2.hconcat().
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Example #1
| Source File: head_pose_solver.py From talking-head-anime-demo with MIT License | 8 votes |
|
def solve_head_pose(self, face_landmarks):
indices = [17, 21, 22, 26, 36, 39, 42, 45, 31, 35]
image_pts = np.zeros((len(indices), 2))
for i in range(len(indices)):
part = face_landmarks.part(indices[i])
image_pts[i, 0] = part.x
image_pts[i, 1] = part.y
_, rotation_vec, translation_vec = cv2.solvePnP(self.face_model_points,
image_pts,
self.camera_matrix,
self.distortion_coeffs)
projected_head_pose_box_points, _ = cv2.projectPoints(self.head_pose_box_points,
rotation_vec,
translation_vec,
self.camera_matrix,
self.distortion_coeffs)
projected_head_pose_box_points = tuple(map(tuple, projected_head_pose_box_points.reshape(8, 2)))
# Calculate euler angle
rotation_mat, _ = cv2.Rodrigues(rotation_vec)
pose_mat = cv2.hconcat((rotation_mat, translation_vec))
_, _, _, _, _, _, euler_angles = cv2.decomposeProjectionMatrix(pose_mat)
return projected_head_pose_box_points, euler_angles
Example #2
| Source File: headpose.py From face_landmark with Apache License 2.0 | 5 votes |
|
def get_head_pose(shape,img):
h,w,_=img.shape
K = [w, 0.0, w//2,
0.0, w, h//2,
0.0, 0.0, 1.0]
# Assuming no lens distortion
D = [0, 0, 0.0, 0.0, 0]
cam_matrix = np.array(K).reshape(3, 3).astype(np.float32)
dist_coeffs = np.array(D).reshape(5, 1).astype(np.float32)
# image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],
# shape[39], shape[42], shape[45], shape[31], shape[35],
# shape[48], shape[54], shape[57], shape[8]])
image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],
shape[39], shape[42], shape[45], shape[31], shape[35]])
_, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs)
reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix,
dist_coeffs)
reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2)))
# calc euler angle
rotation_mat, _ = cv2.Rodrigues(rotation_vec)
pose_mat = cv2.hconcat((rotation_mat, translation_vec))
_, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)
return reprojectdst, euler_angle
Example #3
| Source File: pose.py From Peppa_Pig_Face_Engine with Apache License 2.0 | 5 votes |
|
def get_head_pose(shape,img):
h,w,_=img.shape
K = [w, 0.0, w//2,
0.0, w, h//2,
0.0, 0.0, 1.0]
# Assuming no lens distortion
D = [0, 0, 0.0, 0.0, 0]
cam_matrix = np.array(K).reshape(3, 3).astype(np.float32)
dist_coeffs = np.array(D).reshape(5, 1).astype(np.float32)
# image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],
# shape[39], shape[42], shape[45], shape[31], shape[35],
# shape[48], shape[54], shape[57], shape[8]])
image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],
shape[39], shape[42], shape[45], shape[31], shape[35]])
_, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs)
reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix,
dist_coeffs)
reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2)))
# calc euler angle
rotation_mat, _ = cv2.Rodrigues(rotation_vec)
pose_mat = cv2.hconcat((rotation_mat, translation_vec))
_, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)
return reprojectdst, euler_angle
Example #4
| Source File: headpose.py From PINTO_model_zoo with MIT License | 5 votes |
|
def get_head_pose(shape,img):
h,w,_=img.shape
K = [w, 0.0, w//2,
0.0, w, h//2,
0.0, 0.0, 1.0]
# Assuming no lens distortion
D = [0, 0, 0.0, 0.0, 0]
cam_matrix = np.array(K).reshape(3, 3).astype(np.float32)
dist_coeffs = np.array(D).reshape(5, 1).astype(np.float32)
# image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],
# shape[39], shape[42], shape[45], shape[31], shape[35],
# shape[48], shape[54], shape[57], shape[8]])
image_pts = np.float32([shape[17], shape[21], shape[22], shape[26], shape[36],
shape[39], shape[42], shape[45], shape[31], shape[35]])
_, rotation_vec, translation_vec = cv2.solvePnP(object_pts, image_pts, cam_matrix, dist_coeffs)
reprojectdst, _ = cv2.projectPoints(reprojectsrc, rotation_vec, translation_vec, cam_matrix,
dist_coeffs)
reprojectdst = tuple(map(tuple, reprojectdst.reshape(8, 2)))
# calc euler angle
rotation_mat, _ = cv2.Rodrigues(rotation_vec)
pose_mat = cv2.hconcat((rotation_mat, translation_vec))
_, _, _, _, _, _, euler_angle = cv2.decomposeProjectionMatrix(pose_mat)
return reprojectdst, euler_angle
Example #5
| Source File: movie_concat.py From PINTO_model_zoo with MIT License | 5 votes |
|
def image_hcombine(im_info1, im_info2):
img1 = im_info1[0]
img2 = im_info2[0]
color_flag1 = im_info1[1]
color_flag2 = im_info2[1]
if color_flag1 == 1:
h1, w1, ch1 = img1.shape[:3]
else:
h1, w1 = img1.shape[:2]
if color_flag2 == 1:
h2, w2, ch2 = img2.shape[:3]
else:
h2, w2 = img2.shape[:2]
if h1 < h2:
h1 = h2
w1 = int((h2 / h1) * w2)
img1 = cv2.resize(img1, (w1, h1))
else:
h2 = h1
w2 = int((h1 / h2) * w1)
img2 = cv2.resize(img2, (w2, h2))
img = cv2.hconcat([img1, img2])
return img
Example #6
| Source File: raidnearby.py From PGSS with GNU General Public License v3.0 | 5 votes |
|
def detectTime(self, time_binary):
# img_gray = cv2.cvtColor(time_img, cv2.COLOR_BGR2GRAY)
# ret, thresh1 = cv2.threshold(img_gray, 230, 255, cv2.THRESH_BINARY_INV)
final_img = np.zeros((time_binary.shape[0], int(time_binary.shape[1] * 0.25)), np.uint8)
right_img = np.zeros((time_binary.shape[0], int(time_binary.shape[1] * 0.15)), np.uint8)
separate_img = np.zeros((time_binary.shape[0], int(time_binary.shape[1] * 0.1)), np.uint8)
profile = []
letter_start = []
letter_end = []
count = 0
valley_threshold = 256
# get letters separation pixels
for i in range(time_binary.shape[1]):
sum_vertical = sum(time_binary[:, i])
profile.append(sum_vertical)
if len(letter_start) == len(letter_end):
if sum_vertical > valley_threshold:
letter_start.append(i)
else:
if sum_vertical <= valley_threshold:
letter_end.append(i)
count = count + 1
# Add blank(black) space between letters
for i in range(count):
final_img = cv2.hconcat([final_img, time_binary[0:time_binary.shape[0], letter_start[i]:letter_end[i]]])
final_img = cv2.hconcat([final_img, separate_img])
final_img = cv2.hconcat([final_img, right_img])
kernel = np.ones((2, 2), np.uint8)
final_img = cv2.dilate(final_img, kernel, iterations=1)
cv2.imwrite(self.timefile, final_img)
text = pytesseract.image_to_string(Image.open(self.timefile),
config='-c tessedit_char_whitelist=1234567890:~-AMP -psm 7')
return text
Example #7
| Source File: refiner.py From eccv18-rgb_pose_refinement with MIT License | 5 votes |
|
def iterative_contour_alignment(self, refinable, max_iterations=3,
min_rotation_displacement=0.5,
min_translation_displacement=0.0025, display=False):
assert refinable is not None
last_pose = np.copy(refinable.hypo_pose)
for i in range(max_iterations):
refinable = self.refine(refinable=refinable)
last_trans = last_pose[:3, 3]
last_rot = Quaternion(matrix2quaternion(last_pose[:3, :3]))
cur_trans = refinable.hypo_pose[:3, 3]
cur_rot = Quaternion(matrix2quaternion(refinable.hypo_pose[:3, :3]))
trans_diff = np.linalg.norm(cur_trans - last_trans)
update_q = cur_rot * last_rot.inverse
angular_diff = np.abs((update_q).degrees)
last_pose = np.copy(refinable.hypo_pose)
if display:
concat = cv2.hconcat([refinable.input_col, refinable.hypo_col])
cv2.imshow('test', concat)
cv2.waitKey(500)
if angular_diff <= min_rotation_displacement and trans_diff <= min_translation_displacement:
refinable.iterations = i+1
return refinable
refinable.iterations = max_iterations
return refinable
Example #8
| Source File: utils.py From EndoscopyDepthEstimation-Pytorch with GNU General Public License v3.0 | 4 votes |
|
def generate_training_output(colors_1, scaled_depth_maps_1, boundaries, intrinsic_matrices, is_hsv, epoch,
results_root):
color_inputs_cpu = colors_1.data.cpu().numpy()
pred_depths_cpu = scaled_depth_maps_1.data.cpu().numpy()
boundaries_cpu = boundaries.data.cpu().numpy()
intrinsics_cpu = intrinsic_matrices.data.cpu().numpy()
color_imgs = []
pred_depth_imgs = []
for j in range(colors_1.shape[0]):
color_img = color_inputs_cpu[j]
pred_depth_img = pred_depths_cpu[j]
color_img = np.moveaxis(color_img, source=[0, 1, 2], destination=[2, 0, 1])
color_img = color_img * 0.5 + 0.5
color_img[color_img < 0.0] = 0.0
color_img[color_img > 1.0] = 1.0
color_img = np.uint8(255 * color_img)
if is_hsv:
color_img = cv2.cvtColor(color_img, cv2.COLOR_HSV2BGR_FULL)
pred_depth_img = np.moveaxis(pred_depth_img, source=[0, 1, 2], destination=[2, 0, 1])
if j == 0:
# Write point cloud
boundary = boundaries_cpu[j]
intrinsic = intrinsics_cpu[j]
boundary = np.moveaxis(boundary, source=[0, 1, 2], destination=[2, 0, 1])
point_cloud = point_cloud_from_depth(pred_depth_img, color_img, boundary,
intrinsic,
point_cloud_downsampling=1)
write_point_cloud(
str(results_root / "point_cloud_epoch_{epoch}_index_{index}.ply".format(epoch=epoch,
index=j)),
point_cloud)
color_img = cv2.resize(color_img, dsize=(300, 300))
pred_depth_img = cv2.resize(pred_depth_img, dsize=(300, 300))
color_imgs.append(color_img)
if j == 0:
histr = cv2.calcHist([pred_depth_img], [0], None, histSize=[100], ranges=[0, 1000])
plt.plot(histr, color='b')
plt.xlim([0, 40])
plt.savefig(
str(results_root / 'generated_depth_hist_{epoch}.jpg'.format(epoch=epoch)))
plt.clf()
display_depth_img = display_depth_map(pred_depth_img)
pred_depth_imgs.append(display_depth_img)
final_color = color_imgs[0]
final_pred_depth = pred_depth_imgs[0]
for j in range(colors_1.shape[0] - 1):
final_color = cv2.hconcat((final_color, color_imgs[j + 1]))
final_pred_depth = cv2.hconcat((final_pred_depth, pred_depth_imgs[j + 1]))
final = cv2.vconcat((final_color, final_pred_depth))
cv2.imwrite(str(results_root / 'generated_mask_{epoch}.jpg'.format(epoch=epoch)),
final)
