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run_ssd.py
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run_ssd.py
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# converted from SSD.ipynb
# In[1]:
from keras.applications.imagenet_utils import preprocess_input
from keras.backend.tensorflow_backend import set_session
from keras.preprocessing import image
import matplotlib.pyplot as plt
import numpy as np
from scipy.misc import imread
import tensorflow as tf
from ssd_v2 import SSD300v2
from ssd_utils import BBoxUtility
np.set_printoptions(suppress=True)
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.45
set_session(tf.Session(config=config))
# In[2]:
voc_classes = ['Aeroplane', 'Bicycle', 'Bird', 'Boat', 'Bottle',
'Bus', 'Car', 'Cat', 'Chair', 'Cow', 'Diningtable',
'Dog', 'Horse','Motorbike', 'Person', 'Pottedplant',
'Sheep', 'Sofa', 'Train', 'Tvmonitor']
NUM_CLASSES = len(voc_classes) + 1
# In[3]:
input_shape = (300, 300, 3)
model = SSD300v2(input_shape, num_classes=NUM_CLASSES)
model.load_weights('weights_SSD300.hdf5', by_name=True)
bbox_util = BBoxUtility(NUM_CLASSES)
# In[4]:
inputs = []
images = []
img_path = './pics/fish-bike.jpg'
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
img_path = './pics/cat.jpg'
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
img_path = './pics/boys.jpg'
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
img_path = './pics/car_cat.jpg'
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
img_path = './pics/car_cat2.jpg'
img = image.load_img(img_path, target_size=(300, 300))
img = image.img_to_array(img)
images.append(imread(img_path))
inputs.append(img.copy())
inputs = preprocess_input(np.array(inputs))
# In[5]:
preds = model.predict(inputs, batch_size=1, verbose=1)
# In[6]:
results = bbox_util.detection_out(preds)
# In[8]:
for i, img in enumerate(images):
# Parse the outputs.
det_label = results[i][:, 0]
det_conf = results[i][:, 1]
det_xmin = results[i][:, 2]
det_ymin = results[i][:, 3]
det_xmax = results[i][:, 4]
det_ymax = results[i][:, 5]
# Get detections with confidence higher than 0.6.
top_indices = [i for i, conf in enumerate(det_conf) if conf >= 0.6]
top_conf = det_conf[top_indices]
top_label_indices = det_label[top_indices].tolist()
top_xmin = det_xmin[top_indices]
top_ymin = det_ymin[top_indices]
top_xmax = det_xmax[top_indices]
top_ymax = det_ymax[top_indices]
colors = plt.cm.hsv(np.linspace(0, 1, 21)).tolist()
plt.imshow(img / 255.)
currentAxis = plt.gca()
for i in range(top_conf.shape[0]):
xmin = int(round(top_xmin[i] * img.shape[1]))
ymin = int(round(top_ymin[i] * img.shape[0]))
xmax = int(round(top_xmax[i] * img.shape[1]))
ymax = int(round(top_ymax[i] * img.shape[0]))
score = top_conf[i]
label = int(top_label_indices[i])
label_name = voc_classes[label - 1]
display_txt = '{:0.2f}, {}'.format(score, label_name)
coords = (xmin, ymin), xmax-xmin+1, ymax-ymin+1
color = colors[label]
currentAxis.add_patch(plt.Rectangle(*coords, fill=False, edgecolor=color, linewidth=2))
currentAxis.text(xmin, ymin, display_txt, bbox={'facecolor':color, 'alpha':0.5})
plt.show()