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group_shape_prediction.py
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group_shape_prediction.py
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import numpy as np
import torch
import cv2
from grouping import Grouping
from group_shape_generation import GroupShapeGeneration
from img_process import ProcessImage, DrawGroupShape
from model import ConvAutoencoder
class GroupShapePrediction(object):
def __init__(self, msg):
# No need to do grouping here for msg
self.msg = msg
return
def _load_parameters(self):
# Initialize parameters to prepare for DBSCAN
pos = 2.0
ori = 30
vel = 1.0
params = {'position_threshold': pos,
'orientation_threshold': ori / 180.0 * np.pi,
'velocity_threshold': vel,
'velocity_ignore_threshold': 0.5}
return params
def _predict_sequence(self, input_sequence, pred_length):
cuda = torch.device('cuda:0')
ckpt = 'checkpoints/model_0_200.pth'
model = ConvAutoencoder()
model.load_state_dict(torch.load(ckpt))
model.eval()
model.to(cuda)
output_sequence = []
for i in range(pred_length):
inputs = np.transpose(np.array(input_sequence), (3, 0, 1, 2))
inputs_tensor = np.expand_dims(inputs, 0)
inputs_tensor = torch.tensor(inputs_tensor, dtype=torch.float32, device=cuda)
outputs_tensor = model(inputs_tensor)
outputs = outputs_tensor.data.cpu().numpy()
outputs = np.transpose(outputs[0, :, :, :], (1, 2, 0))
output_sequence.append(outputs)
input_sequence = input_sequence[1:]
input_sequence.append(outputs)
return output_sequence
def predict(self, positions, velocities):
params = self._load_parameters()
position_array = []
velocity_array = []
num_people = len(positions)
if num_people == 0:
raise Exception('People Needed!')
seq_length = len(positions[0])
pred_seq_length = 8
#gp = Grouping(self.msg, seq_length)
#self.msg = gp.update_message(self.msg)
for i in range(num_people):
position_array.append(positions[i][-1])
velocity_array.append(velocities[i][-1])
labels = Grouping.grouping(position_array, velocity_array, params)
all_labels = np.unique(labels)
num_groups = len(all_labels)
all_pred_img_sequences = []
#gsg = GroupShapeGeneration(self.msg)
for curr_label in all_labels:
group_positions = []
group_velocities = []
for i, l in enumerate(labels):
if l == curr_label:
group_positions.append(positions[i])
group_velocities.append(velocities[i])
vertice_sequence = []
for i in range(seq_length):
frame_positions = []
frame_velocities = []
for j in range(len(group_positions)):
frame_positions.append(group_positions[j][i])
frame_velocities.append(group_velocities[j][i])
vertices = GroupShapeGeneration.draw_social_shapes(frame_positions,
frame_velocities)
vertice_sequence.append(vertices)
dgs = DrawGroupShape(self.msg)
dgs.set_center(vertice_sequence)
dgs.set_aug(angle=0)
img_sequence = []
for i, v in enumerate(vertice_sequence):
canvas = np.zeros((self.msg.frame_height, self.msg.frame_width, 3), dtype=np.uint8)
img = dgs.draw_group_shape(v, canvas, center=True, aug=False)
img_sequence.append(img)
pimg = ProcessImage(self.msg, img_sequence)
for i, img in enumerate(img_sequence):
img_sequence[i] = pimg.process_image(img, debug=False)
pred_img_sequence = self._predict_sequence(img_sequence, pred_seq_length)
group_pred_img_sequence = []
for i, img in enumerate(pred_img_sequence):
#img = np.round(np.repeat(img, 3, axis=2)) * 255
img = np.round(np.repeat(img, 3, axis=2))
pred_img = pimg.reverse_process_image(img, debug=True)
pred_img = dgs.reverse_move_center_img(pred_img)
group_pred_img_sequence.append(pred_img[:, :, 0])
all_pred_img_sequences.append(group_pred_img_sequence)
fnl_pred_img_sequence = []
for i in range(pred_seq_length):
canvas = np.zeros((self.msg.frame_height, self.msg.frame_width), dtype=np.uint8)
for j in range(num_groups):
img = all_pred_img_sequences[j][i]
#img = np.round(img)
canvas += img
fnl_pred_img_sequence.append(np.clip(canvas, 0, 1))
return fnl_pred_img_sequence