def preprocess_image(img_path, json_path=None):
#img = io.imread(img_path)
#img = Image.fromarray(img)
img = Image.open(img_path)
img = img.resize((64,128))
img = np.array(img)
#img = resize(img, (128 , 64))
if img.shape[2] == 4:
img = img[:, :, :3]
if json_path is None:
if np.max(img.shape[:2]) != config.img_size:
print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = op_util.get_bbox(json_path)
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
return crop, proc_param, img
def preprocess_image(img, json_path=None):
"""
Crops and rescales image - this function was given (my own bb crop code is separate)
"""
if img.shape[2] == 4:
img = img[:, :, :3]
if json_path is None:
if np.max(img.shape[:2]) != config.img_size:
print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = op_util.get_bbox(json_path)
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
return crop, proc_param, img
def detection(pipe_img, pipe_center, pipe_scale, pipe_img_2, pipe_kp):
params = set_params()
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
detection_count = 0
detection_time = time.time()
while True:
img = pipe_img.recv()
datum = op.Datum()
datum.cvInputData = img
opWrapper.emplaceAndPop([datum])
bodyKeypoints_img = datum.cvOutputData
cv2.rectangle(bodyKeypoints_img, (330, 620), (630, 720), (0, 0, 255),
3)
#cv2.imwrite('kps.jpg',bodyKeypoints_img)
json_path = glob.glob('/media/ramdisk/output_op/*keypoints.json')
scale, center = op_util.get_bbox(json_path[0])
if scale == -1 and center == -1: continue
if scale >= 10: continue
pipe_img_2.send(img)
pipe_center.send(center)
pipe_scale.send(scale)
pipe_kp.send(bodyKeypoints_img)
os.system("rm /media/ramdisk/output_op/*keypoints.json")
detection_count = detection_count + 1
if detection_count == 100:
print('Detection FPS:',
1.0 / ((time.time() - detection_time) / 100.0))
detection_count = 0
detection_time = time.time()
def preprocess_image(img_path, target_size, json_path=None):
crops = []
params = []
imgs = []
for img_name in sorted(os.listdir(img_path)):
if not img_name.endswith('.jpg'):
continue
img = io.imread(os.path.join(img_path, img_name))
if img.shape[2] == 4:
img = img[:, :, :3]
if json_path is None:
if np.max(img.shape[:2]) != target_size:
print('Resizing so the max image size is %d..' % target_size)
scale = (float(target_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = op_util.get_bbox(os.path.join(json_path, img_name))
crop, proc_param = img_util.scale_and_crop(img, scale, center,
target_size)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
crops.append(crop)
params.append(proc_param)
imgs.append(img)
return crops, params, imgs
def preprocess_image(img_path, json_path=None):
img = io.imread(img_path)
print("----- image shape convert -----")
print(img.strides)
if img.shape[2] == 4:
img = img[:, :, :3]
if json_path is None:
if np.max(img.shape[:2]) != config.img_size:
print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = op_util.get_bbox(json_path)
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
print(crop.strides)
print(crop.size)
print(crop.shape)
print(dir(crop))
return crop, proc_param, img
def preprocess_image(img_path, json_path=None):
img = io.imread(img_path) # img is nparr. Yusssss
#print("img.shape:\n{0}\n\n".format(img.shape)) # original shape
if img.shape[2] == 4:
img = img[:, :, :3]
if json_path is None:
if np.max(img.shape[:2]) != config.img_size:
print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = openpose.get_bbox(json_path)
print("using openpose keypoints json...")
print("scale: ", scale) # 0.12
print("center: ", center)
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
print("crop.size:", crop.size)
pltshow(
crop
) # for my Dropbox/vr_mall_backup/IMPORTANT/front.jpg image, this crop did something real weird to it. Might be because the openpose keypoints are in a different order?? (HMR & Kanazawa are using 1.0 whereas I'm using 1.2)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
pltshow(crop)
return crop, proc_param, img
def preprocess_image(img_path, json_path=None):
img = io.imread(img_path)
if len(img.shape) == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2RGB)
if img.shape[2] == 4:
img = img[:, :, :3]
if json_path is None:
if np.max(img.shape[:2]) != config.img_size:
print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = op_util.get_bbox(json_path)
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
return crop, proc_param, img
def preprocess_image(img,
depth,
json_path=None,
joints2d_gt=None,
cam_gt=None):
#img = io.imread(img_path)
#if img.shape[2] == 4:
# img = img[:, :, :3]
#if depth_path is not None:
# if ".pfm" in depth_path:
# dep = pfm.load_pfm(depth_path)
# else:
# dep = io.imread(depth_path)
#else:
# dep = np.zeros(img.size, dtype = np.float32)
if img.shape[2] == 4:
img = img[:, :, :3]
depth = np.reshape(depth, [depth.shape[0], depth.shape[1], 1])
img_orig = img
img = np.concatenate([img, depth], -1)
if json_path is None:
if np.max(img.shape[:2]) != config.img_size:
#print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = op_util.get_bbox(json_path)
if joints2d_gt is not None:
crop, proc_param, joints2d_gt_scaled, cam_gt_scaled = img_util.scale_and_crop_with_gt(
img, scale, center, config.img_size, joints2d_gt, cam_gt)
else:
joints2d_gt_scaled = None
cam_gt_scaled = None
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
# Normalize image to [-1, 1]
crop_img = crop[:, :, 0:3]
crop_depth = np.reshape(crop[:, :, 3], [crop.shape[0], crop.shape[1], 1])
crop_img = 2 * ((crop_img / 255.) - 0.5)
depth_max = np.max(crop_depth)
crop_depth = 2.0 * (crop_depth / depth_max - 0.5)
return crop_img, crop_depth, proc_param, img_orig, joints2d_gt_scaled, cam_gt_scaled
def preprocess_image(img_path, json_path=None):
img = io.imread(img_path)
if json_path is None:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = op_util.get_bbox(json_path)
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
return crop, proc_param, img
def detection(pipe_img, pipe_center, pipe_scale, pipe_shape, pipe_img_2,
pipe_kp):
params = set_params()
opWrapper = op.WrapperPython()
opWrapper.configure(params)
opWrapper.start()
detection_count = 0
detection_time = time.time()
while True:
img = pipe_img.recv()
datum = op.Datum()
datum.cvInputData = img
opWrapper.emplaceAndPop([datum])
bodyKeypoints_img = datum.cvOutputData
#cv2.rectangle(bodyKeypoints_img,(330,50),(630,720),(0,0,255),1)
#cv2.rectangle(bodyKeypoints_img,(330,630),(630,720),(0,0,255),3)
cv2.imwrite('/media/ramdisk/kps.jpg', bodyKeypoints_img)
str_img_kps = base64.b64encode(
open('/media/ramdisk/kps.jpg', 'rb').read())
message_id = queue1.sendMessage(delay=0).message(
str_img_kps.decode('utf-8')).execute()
msg1.append(message_id)
if len(msg1) > 1:
rt = queue1.deleteMessage(id=msg1[0]).execute()
del msg1[0]
json_path = glob.glob('/media/ramdisk/output_op/*keypoints.json')
scale, center, person_shape = op_util.get_bbox(json_path[0])
if scale == -1 and center == -1 and person_shape == -1: continue
if scale >= 10: continue
pipe_img_2.send(img)
pipe_center.send(center)
pipe_scale.send(scale)
pipe_shape.send(person_shape)
pipe_kp.send(bodyKeypoints_img)
os.system("rm /media/ramdisk/output_op/*keypoints.json")
detection_count = detection_count + 1
if detection_count == 100:
print('Detection FPS:',
1.0 / ((time.time() - detection_time) / 100.0))
detection_count = 0
detection_time = time.time()
def preprocess_image_nathan(img, json_path=None):
print("img.shape:\n{0}\n\n".format(img.shape))
if img.shape[2] == 4:
img = img[:, :, :3]
if json_path is None:
if np.max(img.shape[:2]) != config.img_size:
print('Resizing so the max image size is %d..' % config.img_size)
scale = (float(config.img_size) / np.max(img.shape[:2]))
else:
scale = 1.
center = np.round(np.array(img.shape[:2]) / 2).astype(int)
# image center in (x,y)
center = center[::-1]
else:
scale, center = openpose.get_bbox(json_path)
crop, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
# Normalize image to [-1, 1]
crop = 2 * ((crop / 255.) - 0.5)
return crop, proc_param, img # what the f**k was Kanazawa even using this 'img' variable at the end for? Maybe it's just left over from old code.
json_file.write(json.dumps(data))
print('camera pose writed!')
while True:
t0 = time.time()
try:
img = io.imread(config.img_path)
if img.shape[2] == 4:
img = img[:, :, :3]
except IOError:
print("image not found, try again!")
continue
else:
print("image load success!")
scale, center = op_util.get_bbox(config.json_path)
if scale == -1 and center == -1: continue
if scale >= 10: continue
#print(111, scale, center, config.img_size)
input_img, proc_param = img_util.scale_and_crop(img, scale, center,
config.img_size)
input_img = 2 * ((input_img / 255.) - 0.5)
input_img = np.expand_dims(input_img, 0)
joints, verts, cams, joints3d, theta = model.predict(input_img,
get_theta=True)
#print('3D Rec:', time.time() - t0)
cam_for_render, vert_shifted, joints_orig = vis_util.get_original(
proc_param, verts[0], cams[0], joints[0], img_size=img.shape[:2])
#print('3D Rec:', time.time() - t0)
#print('type(cam_for_render):', type(cam_for_render))
#print(img.shape[:2])