def test_safe_crop(self):
name = 'SUNRGBD/kv1/NYUdata/NYU0899'
image, image_size = get_image(name)
semantic = get_category(name, image_size)
different_sizes = [(320, 320), (480, 480), (640, 640)]
crop_size = random.choice(different_sizes)
x, y = random_choice(image_size, crop_size)
image = safe_crop(image, x, y, crop_size)
semantic = safe_crop(semantic, x, y, crop_size)
semantic = to_bgr(semantic)
cv.imwrite('temp/test_safe_crop_image.png', image)
cv.imwrite('temp/test_safe_crop_semantic.png', semantic)
def classify_image(image_data):
img_array = np.fromstring(image_data, dtype=np.uint8)
image = cv.imdecode(img_array, cv.IMREAD_COLOR)
image_size = image.shape[:2]
x, y = random_choice(image_size)
image = safe_crop(image, x, y)
print('Start processing image...')
x_test = np.empty((1, img_rows, img_cols, 3), dtype=np.float32)
x_test[0, :, :, 0:3] = image / 255.
out = model.predict(x_test)
out = np.reshape(out, (img_rows, img_cols, num_classes))
out = np.argmax(out, axis=2)
out = to_bgr(out)
ret = image * 0.6 + out * 0.4
ret = ret.astype(np.uint8)
K.clear_session()
is_success, buffer = cv.imencode(".png", out)
buffer
out_bytes = BytesIO(buffer)
return out_bytes
filename = './single_human_paring/img/man/unexpose/img_023.jpg'
img_rows, img_cols = 320, 320
channel = 3
model_weights_path = 'Look_Into_Person/models/model.11-0.8409.hdf5'
model = build_model()
model.load_weights(model_weights_path)
print(model.summary())
image = cv.imread(filename)
image = cv.resize(image, (img_rows, img_cols), cv.INTER_CUBIC)
image_size = image.shape[:2]
x, y = random_choice(image_size)
image = safe_crop(image, x, y)
print('Start processing image: {}'.format(filename))
x_test = np.empty((1, img_rows, img_cols, 3), dtype=np.float32)
x_test[0, :, :, 0:3] = image / 255.
out = model.predict(x_test)
out = np.reshape(out, (img_rows, img_cols, num_classes))
out = np.argmax(out, axis=2)
out = to_bgr(out)
#
# ret = image * 0.6 + out * 0.4
# ret = ret.astype(np.uint8)
#if not os.path.exists('images'):
# os.makedirs('images')
os.path.isfile(os.path.join(rgb_test_path, f)) and f.endswith('.png')]
samples = random.sample(test_images, 10)
for i in range(len(samples)):
image_name = samples[i]
filename = os.path.join(rgb_test_path, image_name)
image = cv.imread(filename)
label = get_semantic(image_name)
image_size = image.shape[:2]
different_sizes = [(320, 320), (480, 480), (480, 480), (480, 480), (640, 640), (640, 640), (640, 640),
(960, 960), (960, 960), (960, 960)]
crop_size = random.choice(different_sizes)
x, y = random_choice(image_size, crop_size)
image = safe_crop(image, x, y, crop_size)
label = safe_crop(label, x, y, crop_size)
print('Start processing image: {}'.format(filename))
x_test = np.empty((1, img_rows, img_cols, 3), dtype=np.float32)
x_test[0, :, :, 0:3] = image / 255.
out = model.predict(x_test)
out = np.reshape(out, (img_rows, img_cols, num_classes))
out = np.argmax(out, axis=2)
out = to_bgr(out)
str_msg = 'crop_size: %s' % (str(crop_size))
draw_str(out, (20, 20), str_msg)
if not os.path.exists('images'):