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yolo.py
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import argparse
import os
import numpy as np
from keras.layers import Conv2D, Input, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D
from keras.layers.merge import add, concatenate
from keras.models import Model
import struct
import cv2
np.set_printoptions(threshold=np.nan)
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
argparser = argparse.ArgumentParser(
description='test yolov3 network with coco weights')
argparser.add_argument(
'-w',
'--weights',
help='path to weights file')
argparser.add_argument(
'-i',
'--image',
help='path to image file')
class WeightReader:
def __init__(self, weight_file):
with open(weight_file, 'rb') as w_f:
major, = struct.unpack('i', w_f.read(4))
minor, = struct.unpack('i', w_f.read(4))
revision, = struct.unpack('i', w_f.read(4))
if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:
w_f.read(8)
else:
w_f.read(4)
transpose = (major > 1000) or (minor > 1000)
binary = w_f.read()
self.offset = 0
self.all_weights = np.frombuffer(binary, dtype='float32')
def read_bytes(self, size):
self.offset = self.offset + size
return self.all_weights[self.offset-size:self.offset]
def load_weights(self, model):
for i in range(106):
try:
conv_layer = model.get_layer('conv_' + str(i))
print("loading weights of convolution #" + str(i))
if i not in [81, 93, 105]:
norm_layer = model.get_layer('bnorm_' + str(i))
size = np.prod(norm_layer.get_weights()[0].shape)
beta = self.read_bytes(size) # bias
gamma = self.read_bytes(size) # scale
mean = self.read_bytes(size) # mean
var = self.read_bytes(size) # variance
weights = norm_layer.set_weights([gamma, beta, mean, var])
if len(conv_layer.get_weights()) > 1:
bias = self.read_bytes(
np.prod(conv_layer.get_weights()[1].shape))
kernel = self.read_bytes(
np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(
list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2, 3, 1, 0])
conv_layer.set_weights([kernel, bias])
else:
kernel = self.read_bytes(
np.prod(conv_layer.get_weights()[0].shape))
kernel = kernel.reshape(
list(reversed(conv_layer.get_weights()[0].shape)))
kernel = kernel.transpose([2, 3, 1, 0])
conv_layer.set_weights([kernel])
except ValueError:
print("no convolution #" + str(i))
def reset(self):
self.offset = 0
class BoundBox:
def __init__(self, xmin, ymin, xmax, ymax, objness=None, classes=None):
self.xmin = xmin
self.ymin = ymin
self.xmax = xmax
self.ymax = ymax
self.objness = objness
self.classes = classes
self.label = -1
self.score = -1
def get_label(self):
if self.label == -1:
self.label = np.argmax(self.classes)
return self.label
def get_score(self):
if self.score == -1:
self.score = self.classes[self.get_label()]
return self.score
def _conv_block(inp, convs, skip=True):
x = inp
count = 0
for conv in convs:
if count == (len(convs) - 2) and skip:
skip_connection = x
count += 1
if conv['stride'] > 1:
x = ZeroPadding2D(((1, 0), (1, 0)))(x)
x = Conv2D(conv['filter'],
conv['kernel'],
strides=conv['stride'],
padding='valid' if conv['stride'] > 1 else 'same',
name='conv_' + str(conv['layer_idx']),
use_bias=False if conv['bnorm'] else True)(x)
if conv['bnorm']:
x = BatchNormalization(
epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)
if conv['leaky']:
x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)
return add([skip_connection, x]) if skip else x
def _interval_overlap(interval_a, interval_b):
x1, x2 = interval_a
x3, x4 = interval_b
if x3 < x1:
if x4 < x1:
return 0
else:
return min(x2, x4) - x1
else:
if x2 < x3:
return 0
else:
return min(x2, x4) - x3
def _sigmoid(x):
return 1. / (1. + np.exp(-x))
def bbox_iou(box1, box2):
intersect_w = _interval_overlap(
[box1.xmin, box1.xmax], [box2.xmin, box2.xmax])
intersect_h = _interval_overlap(
[box1.ymin, box1.ymax], [box2.ymin, box2.ymax])
intersect = intersect_w * intersect_h
w1, h1 = box1.xmax-box1.xmin, box1.ymax-box1.ymin
w2, h2 = box2.xmax-box2.xmin, box2.ymax-box2.ymin
union = w1*h1 + w2*h2 - intersect
return float(intersect) / union
def make_yolov3_model():
input_image = Input(shape=(None, None, 3))
# Layer 0 => 4
x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},
{'filter': 64, 'kernel': 3, 'stride': 2,
'bnorm': True, 'leaky': True, 'layer_idx': 1},
{'filter': 32, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 2},
{'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])
# Layer 5 => 8
x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},
{'filter': 64, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 6},
{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])
# Layer 9 => 11
x = _conv_block(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},
{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])
# Layer 12 => 15
x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},
{'filter': 128, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 13},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])
# Layer 16 => 36
for i in range(7):
x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},
{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])
skip_36 = x
# Layer 37 => 40
x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},
{'filter': 256, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 38},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])
# Layer 41 => 61
for i in range(7):
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},
{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])
skip_61 = x
# Layer 62 => 65
x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},
{'filter': 512, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 63},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])
# Layer 66 => 74
for i in range(3):
x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},
{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])
# Layer 75 => 79
x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},
{'filter': 1024, 'kernel': 3, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 76},
{'filter': 512, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 77},
{'filter': 1024, 'kernel': 3, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 78},
{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}], skip=False)
# Layer 80 => 82
yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 80},
{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}], skip=False)
# Layer 83 => 86
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 84}], skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_61])
# Layer 87 => 91
x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},
{'filter': 512, 'kernel': 3, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 88},
{'filter': 256, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 89},
{'filter': 512, 'kernel': 3, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 90},
{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 91}], skip=False)
# Layer 92 => 94
yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 92},
{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}], skip=False)
# Layer 95 => 98
x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 96}], skip=False)
x = UpSampling2D(2)(x)
x = concatenate([x, skip_36])
# Layer 99 => 106
yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 99},
{'filter': 256, 'kernel': 3, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 100},
{'filter': 128, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 101},
{'filter': 256, 'kernel': 3, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 102},
{'filter': 128, 'kernel': 1, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 103},
{'filter': 256, 'kernel': 3, 'stride': 1,
'bnorm': True, 'leaky': True, 'layer_idx': 104},
{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 105}], skip=False)
model = Model(input_image, [yolo_82, yolo_94, yolo_106])
return model
def preprocess_input(image, net_h, net_w):
new_h, new_w, _ = image.shape
if (float(net_w)/new_w) < (float(net_h)/new_h):
new_h = (new_h * net_w)/new_w
new_w = net_w
else:
new_w = (new_w * net_h)/new_h
new_h = net_h
resized = cv2.resize(image[:, :, ::-1]/255., (int(new_w), int(new_h)))
new_image = np.ones((net_h, net_w, 3)) * 0.5
new_image[int((net_h-new_h)//2):int((net_h+new_h)//2),
int((net_w-new_w)//2):int((net_w+new_w)//2), :] = resized
new_image = np.expand_dims(new_image, 0)
return new_image
def decode_netout(netout, anchors, obj_thresh, nms_thresh, net_h, net_w):
grid_h, grid_w = netout.shape[:2]
nb_box = 3
netout = netout.reshape((grid_h, grid_w, nb_box, -1))
nb_class = netout.shape[-1] - 5
boxes = []
netout[..., :2] = _sigmoid(netout[..., :2])
netout[..., 4:] = _sigmoid(netout[..., 4:])
netout[..., 5:] = netout[..., 4][..., np.newaxis] * netout[..., 5:]
netout[..., 5:] *= netout[..., 5:] > obj_thresh
for i in range(grid_h*grid_w):
row = i / grid_w
col = i % grid_w
for b in range(nb_box):
objectness = netout[int(row)][int(col)][b][4]
if(objectness.all() <= obj_thresh):
continue
x, y, w, h = netout[int(row)][int(col)][b][:4]
x = (col + x) / grid_w
y = (row + y) / grid_h
w = anchors[2 * b + 0] * np.exp(w) / net_w
h = anchors[2 * b + 1] * np.exp(h) / net_h
classes = netout[int(row)][col][b][5:]
box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, objectness, classes)
#box = BoundBox(x-w/2, y-h/2, x+w/2, y+h/2, None, classes)
boxes.append(box)
return boxes
def correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w):
if (float(net_w)/image_w) < (float(net_h)/image_h):
new_w = net_w
new_h = (image_h*net_w)/image_w
else:
new_h = net_w
new_w = (image_w*net_h)/image_h
for i in range(len(boxes)):
x_offset, x_scale = (net_w - new_w)/2./net_w, float(new_w)/net_w
y_offset, y_scale = (net_h - new_h)/2./net_h, float(new_h)/net_h
boxes[i].xmin = int((boxes[i].xmin - x_offset) / x_scale * image_w)
boxes[i].xmax = int((boxes[i].xmax - x_offset) / x_scale * image_w)
boxes[i].ymin = int((boxes[i].ymin - y_offset) / y_scale * image_h)
boxes[i].ymax = int((boxes[i].ymax - y_offset) / y_scale * image_h)
def do_nms(boxes, nms_thresh):
if len(boxes) > 0:
nb_class = len(boxes[0].classes)
else:
return
for c in range(nb_class):
sorted_indices = np.argsort([-box.classes[c] for box in boxes])
for i in range(len(sorted_indices)):
index_i = sorted_indices[i]
if boxes[index_i].classes[c] == 0:
continue
for j in range(i+1, len(sorted_indices)):
index_j = sorted_indices[j]
if bbox_iou(boxes[index_i], boxes[index_j]) >= nms_thresh:
boxes[index_j].classes[c] = 0
def draw_boxes(image, boxes, labels, obj_thresh):
for box in boxes:
label_str = ''
label = -1
for i in range(len(labels)):
if box.classes[i] > obj_thresh:
label_str += labels[i]
label = i
print(labels[i] + ': ' + str(box.classes[i]*100) + '%')
if label >= 0:
cv2.rectangle(image, (box.xmin, box.ymin),
(box.xmax, box.ymax), (0, 255, 0), 3)
cv2.putText(image,
label_str + ' ' + str(box.get_score()),
(box.xmin, box.ymin - 13),
cv2.FONT_HERSHEY_SIMPLEX,
1e-3 * image.shape[0],
(0, 255, 0), 2)
return image
def _main_(args):
weights_path = args.weights
image_path = args.image
# set some parameters
net_h, net_w = 416, 416
obj_thresh, nms_thresh = 0.5, 0.45
anchors = [[116, 90, 156, 198, 373, 326], [
30, 61, 62, 45, 59, 119], [10, 13, 16, 30, 33, 23]]
labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train", "truck",
"boat", "traffic light", "fire hydrant", "stop sign", "parking meter", "bench",
"bird", "cat", "dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe",
"backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard",
"sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
"tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana",
"apple", "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake",
"chair", "sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cell phone", "microwave", "oven", "toaster", "sink", "refrigerator",
"book", "clock", "vase", "scissors", "teddy bear", "hair drier", "toothbrush"]
yolov3 = make_yolov3_model()
weight_reader = WeightReader(weights_path)
weight_reader.load_weights(yolov3)
image = cv2.imread(image_path)
image_h, image_w, _ = image.shape
new_image = preprocess_input(image, net_h, net_w)
yolos = yolov3.predict(new_image)
boxes = []
for i in range(len(yolos)):
boxes += decode_netout(yolos[i][0], anchors[i],
obj_thresh, nms_thresh, net_h, net_w)
correct_yolo_boxes(boxes, image_h, image_w, net_h, net_w)
do_nms(boxes, nms_thresh)
draw_boxes(image, boxes, labels, obj_thresh)
cv2.imwrite(image_path[:-4] + '_detected' +
image_path[-4:], (image).astype('uint8'))
if __name__ == '__main__':
args = argparser.parse_args()
_main_(args)