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inference_webcam.py
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inference_webcam.py
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"""
EfficientPose (c) by Steinbeis GmbH & Co. KG für Technologietransfer
Haus der Wirtschaft, Willi-Bleicher-Straße 19, 70174 Stuttgart, Germany
Yannick Bukschat: yannick.bukschat@stw.de
Marcus Vetter: marcus.vetter@stw.de
EfficientPose is licensed under a
Creative Commons Attribution-NonCommercial 4.0 International License.
The license can be found in the LICENSE file in the root directory of this source tree
or at http://creativecommons.org/licenses/by-nc/4.0/.
---------------------------------------------------------------------------------------------------------------------------------
---------------------------------------------------------------------------------------------------------------------------------
Based on:
Keras EfficientDet implementation (https://github.com/xuannianz/EfficientDet) licensed under the Apache License, Version 2.0
---------------------------------------------------------------------------------------------------------------------------------
The official EfficientDet implementation (https://github.com/google/automl) licensed under the Apache License, Version 2.0
---------------------------------------------------------------------------------------------------------------------------------
EfficientNet Keras implementation (https://github.com/qubvel/efficientnet) licensed under the Apache License, Version 2.0
---------------------------------------------------------------------------------------------------------------------------------
Keras RetinaNet implementation (https://github.com/fizyr/keras-retinanet) licensed under the Apache License, Version 2.0
"""
import cv2
import numpy as np
import os
import math
import tensorflow as tf
from model import build_EfficientPose
from utils import preprocess_image
from utils.visualization import draw_detections
def main():
"""
Run EfficientPose in inference mode live on webcam.
"""
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
allow_gpu_growth_memory()
#input parameter
phi = 0
path_to_weights = "./weights/phi_0_occlusion_best_ADD(-S).h5"
# save_path = "./predictions/occlusion/" #where to save the images or None if the images should be displayed and not saved
save_path = None
image_extension = ".jpg"
class_to_name = {0: "ape", 1: "can", 2: "cat", 3: "driller", 4: "duck", 5: "eggbox", 6: "glue", 7: "holepuncher"} #Occlusion
#class_to_name = {0: "driller"} #Linemod use a single class with a name of the Linemod objects
score_threshold = 0.5
translation_scale_norm = 1000.0
draw_bbox_2d = False
draw_name = False
#you probably need to replace the linemod camera matrix with the one of your webcam
camera_matrix = get_linemod_camera_matrix()
name_to_3d_bboxes = get_linemod_3d_bboxes()
class_to_3d_bboxes = {class_idx: name_to_3d_bboxes[name] for class_idx, name in class_to_name.items()}
num_classes = len(class_to_name)
#build model and load weights
model, image_size = build_model_and_load_weights(phi, num_classes, score_threshold, path_to_weights)
webcam = cv2.VideoCapture(0)
#inferencing
print("\nStarting inference...\n")
i = 0
while True:
#load image
got_image, image = webcam.read()
if not got_image:
continue
original_image = image.copy()
#preprocessing
input_list, scale = preprocess(image, image_size, camera_matrix, translation_scale_norm)
#predict
boxes, scores, labels, rotations, translations = model.predict_on_batch(input_list)
#postprocessing
boxes, scores, labels, rotations, translations = postprocess(boxes, scores, labels, rotations, translations, scale, score_threshold)
draw_detections(original_image,
boxes,
scores,
labels,
rotations,
translations,
class_to_bbox_3D = class_to_3d_bboxes,
camera_matrix = camera_matrix,
label_to_name = class_to_name,
draw_bbox_2d = draw_bbox_2d,
draw_name = draw_name)
#display image with predictions
cv2.imshow('image with predictions', original_image)
if cv2.waitKey(1) & 0xFF == ord('q'):
break
if not save_path is None:
#images to the given path
os.makedirs(save_path, exist_ok = True)
cv2.imwrite(os.path.join(save_path, "frame_{}".format(i) + image_extension), original_image)
i += 1
#release webcam and close windows
webcam.release()
cv2.destroyAllWindows()
def allow_gpu_growth_memory():
"""
Set allow growth GPU memory to true
"""
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
_ = tf.Session(config = config)
def get_linemod_camera_matrix():
"""
Returns:
The Linemod and Occlusion 3x3 camera matrix
"""
return np.array([[572.4114, 0., 325.2611], [0., 573.57043, 242.04899], [0., 0., 1.]], dtype = np.float32)
def get_linemod_3d_bboxes():
"""
Returns:
name_to_3d_bboxes: Dictionary with the Linemod and Occlusion 3D model names as keys and the cuboids as values
"""
name_to_model_info = {"ape": {"diameter": 102.09865663, "min_x": -37.93430000, "min_y": -38.79960000, "min_z": -45.88450000, "size_x": 75.86860000, "size_y": 77.59920000, "size_z": 91.76900000},
"benchvise": {"diameter": 247.50624233, "min_x": -107.83500000, "min_y": -60.92790000, "min_z": -109.70500000, "size_x": 215.67000000, "size_y": 121.85570000, "size_z": 219.41000000},
"cam": {"diameter": 172.49224865, "min_x": -68.32970000, "min_y": -71.51510000, "min_z": -50.24850000, "size_x": 136.65940000, "size_y": 143.03020000, "size_z": 100.49700000},
"can": {"diameter": 201.40358597, "min_x": -50.39580000, "min_y": -90.89790000, "min_z": -96.86700000, "size_x": 100.79160000, "size_y": 181.79580000, "size_z": 193.73400000},
"cat": {"diameter": 154.54551808, "min_x": -33.50540000, "min_y": -63.81650000, "min_z": -58.72830000, "size_x": 67.01070000, "size_y": 127.63300000, "size_z": 117.45660000},
"driller": {"diameter": 261.47178102, "min_x": -114.73800000, "min_y": -37.73570000, "min_z": -104.00100000, "size_x": 229.47600000, "size_y": 75.47140000, "size_z": 208.00200000},
"duck": {"diameter": 108.99920102, "min_x": -52.21460000, "min_y": -38.70380000, "min_z": -42.84850000, "size_x": 104.42920000, "size_y": 77.40760000, "size_z": 85.69700000},
"eggbox": {"diameter": 164.62758848, "min_x": -75.09230000, "min_y": -53.53750000, "min_z": -34.62070000, "size_x": 150.18460000, "size_y": 107.07500000, "size_z": 69.24140000},
"glue": {"diameter": 175.88933422, "min_x": -18.36050000, "min_y": -38.93300000, "min_z": -86.40790000, "size_x": 36.72110000, "size_y": 77.86600000, "size_z": 172.81580000},
"holepuncher": {"diameter": 145.54287471, "min_x": -50.44390000, "min_y": -54.24850000, "min_z": -45.40000000, "size_x": 100.88780000, "size_y": 108.49700000, "size_z": 90.80000000},
"iron": {"diameter": 278.07811733, "min_x": -129.11300000, "min_y": -59.24100000, "min_z": -70.56620000, "size_x": 258.22600000, "size_y": 118.48210000, "size_z": 141.13240000},
"lamp": {"diameter": 282.60129399, "min_x": -101.57300000, "min_y": -58.87630000, "min_z": -106.55800000, "size_x": 203.14600000, "size_y": 117.75250000, "size_z": 213.11600000},
"phone": {"diameter": 212.35825148, "min_x": -46.95910000, "min_y": -73.71670000, "min_z": -92.37370000, "size_x": 93.91810000, "size_y": 147.43340000, "size_z": 184.74740000}}
name_to_3d_bboxes = {name: convert_bbox_3d(model_info) for name, model_info in name_to_model_info.items()}
return name_to_3d_bboxes
def convert_bbox_3d(model_dict):
"""
Converts the 3D model cuboids from the Linemod format (min_x, min_y, min_z, size_x, size_y, size_z) to the (num_corners = 8, num_coordinates = 3) format
Args:
model_dict: Dictionary containing the cuboid information of a single Linemod 3D model in the Linemod format
Returns:
bbox: numpy (8, 3) array containing the 3D model's cuboid, where the first dimension represents the corner points and the second dimension contains the x-, y- and z-coordinates.
"""
#get infos from model dict
min_point_x = model_dict["min_x"]
min_point_y = model_dict["min_y"]
min_point_z = model_dict["min_z"]
size_x = model_dict["size_x"]
size_y = model_dict["size_y"]
size_z = model_dict["size_z"]
bbox = np.zeros(shape = (8, 3))
#lower level
bbox[0, :] = np.array([min_point_x, min_point_y, min_point_z])
bbox[1, :] = np.array([min_point_x + size_x, min_point_y, min_point_z])
bbox[2, :] = np.array([min_point_x + size_x, min_point_y + size_y, min_point_z])
bbox[3, :] = np.array([min_point_x, min_point_y + size_y, min_point_z])
#upper level
bbox[4, :] = np.array([min_point_x, min_point_y, min_point_z + size_z])
bbox[5, :] = np.array([min_point_x + size_x, min_point_y, min_point_z + size_z])
bbox[6, :] = np.array([min_point_x + size_x, min_point_y + size_y, min_point_z + size_z])
bbox[7, :] = np.array([min_point_x, min_point_y + size_y, min_point_z + size_z])
return bbox
def build_model_and_load_weights(phi, num_classes, score_threshold, path_to_weights):
"""
Builds an EfficientPose model and init it with a given weight file
Args:
phi: EfficientPose scaling hyperparameter
num_classes: The number of classes
score_threshold: Minimum score threshold at which a prediction is not filtered out
path_to_weights: Path to the weight file
Returns:
efficientpose_prediction: The EfficientPose model
image_size: Integer image size used as the EfficientPose input resolution for the given phi
"""
print("\nBuilding model...\n")
_, efficientpose_prediction, _ = build_EfficientPose(phi,
num_classes = num_classes,
num_anchors = 9,
freeze_bn = True,
score_threshold = score_threshold,
num_rotation_parameters = 3,
print_architecture = False)
print("\nDone!\n\nLoading weights...")
efficientpose_prediction.load_weights(path_to_weights, by_name=True)
print("Done!")
image_sizes = (512, 640, 768, 896, 1024, 1280, 1408)
image_size = image_sizes[phi]
return efficientpose_prediction, image_size
def preprocess(image, image_size, camera_matrix, translation_scale_norm):
"""
Preprocesses the inputs for EfficientPose
Args:
image: The image to predict
image_size: Input resolution for EfficientPose
camera_matrix: numpy 3x3 array containing the intrinsic camera parameters
translation_scale_norm: factor to change units. EfficientPose internally works with meter and if your dataset unit is mm for example, then you need to set this parameter to 1000
Returns:
input_list: List containing the preprocessed inputs for EfficientPose
scale: The scale factor of the resized input image and the original image
"""
image = image[:, :, ::-1]
image, scale = preprocess_image(image, image_size)
camera_input = get_camera_parameter_input(camera_matrix, scale, translation_scale_norm)
image_batch = np.expand_dims(image, axis=0)
camera_batch = np.expand_dims(camera_input, axis=0)
input_list = [image_batch, camera_batch]
return input_list, scale
def get_camera_parameter_input(camera_matrix, image_scale, translation_scale_norm):
"""
Return the input vector for the camera parameter layer
Args:
camera_matrix: numpy 3x3 array containing the intrinsic camera parameters
image_scale: The scale factor of the resized input image and the original image
translation_scale_norm: factor to change units. EfficientPose internally works with meter and if your dataset unit is mm for example, then you need to set this parameter to 1000
Returns:
input_vector: numpy array [fx, fy, px, py, translation_scale_norm, image_scale]
"""
#input_vector = [fx, fy, px, py, translation_scale_norm, image_scale]
input_vector = np.zeros((6,), dtype = np.float32)
input_vector[0] = camera_matrix[0, 0]
input_vector[1] = camera_matrix[1, 1]
input_vector[2] = camera_matrix[0, 2]
input_vector[3] = camera_matrix[1, 2]
input_vector[4] = translation_scale_norm
input_vector[5] = image_scale
return input_vector
def postprocess(boxes, scores, labels, rotations, translations, scale, score_threshold):
"""
Filter out detections with low confidence scores and rescale the outputs of EfficientPose
Args:
boxes: numpy array [batch_size = 1, max_detections, 4] containing the 2D bounding boxes
scores: numpy array [batch_size = 1, max_detections] containing the confidence scores
labels: numpy array [batch_size = 1, max_detections] containing class label
rotations: numpy array [batch_size = 1, max_detections, 3] containing the axis angle rotation vectors
translations: numpy array [batch_size = 1, max_detections, 3] containing the translation vectors
scale: The scale factor of the resized input image and the original image
score_threshold: Minimum score threshold at which a prediction is not filtered out
Returns:
boxes: numpy array [num_valid_detections, 4] containing the 2D bounding boxes
scores: numpy array [num_valid_detections] containing the confidence scores
labels: numpy array [num_valid_detections] containing class label
rotations: numpy array [num_valid_detections, 3] containing the axis angle rotation vectors
translations: numpy array [num_valid_detections, 3] containing the translation vectors
"""
boxes, scores, labels, rotations, translations = np.squeeze(boxes), np.squeeze(scores), np.squeeze(labels), np.squeeze(rotations), np.squeeze(translations)
# correct boxes for image scale
boxes /= scale
#rescale rotations
rotations *= math.pi
#filter out detections with low scores
indices = np.where(scores[:] > score_threshold)
# select detections
scores = scores[indices]
boxes = boxes[indices]
rotations = rotations[indices]
translations = translations[indices]
labels = labels[indices]
return boxes, scores, labels, rotations, translations
if __name__ == '__main__':
main()