def __init__(
self,
img,
model_path='./bodypix_resnet50_float_model-stride16/model.json',
output_stride=16):
print("[INFO] Loading model...")
self.graph = load_graph_model(model_path)
print("[INFO] Loaded model...")
self.output_stride = output_stride
self.img = img
# Get input and output tensors
self.input_tensor_names = get_input_tensors(self.graph)
print(self.input_tensor_names)
self.output_tensor_names = get_output_tensors(self.graph)
print(self.output_tensor_names)
self.input_tensor = self.graph.get_tensor_by_name(
self.input_tensor_names[0])
targetHeight = (int(imgHeight) // OutputStride) * OutputStride + 1
print(imgHeight, imgWidth, targetHeight, targetWidth)
img = img.resize((targetWidth, targetHeight))
x = tf.keras.preprocessing.image.img_to_array(img, dtype=np.float32)
InputImageShape = x.shape
print("Input Image Shape in hwc", InputImageShape)
widthResolution = int((InputImageShape[1] - 1) / OutputStride) + 1
heightResolution = int((InputImageShape[0] - 1) / OutputStride) + 1
print('Resolution', widthResolution, heightResolution)
# Get input and output tensors
input_tensor_names = get_input_tensors(graph)
print(input_tensor_names)
output_tensor_names = get_output_tensors(graph)
print(output_tensor_names)
input_tensor = graph.get_tensor_by_name(input_tensor_names[0])
# Preprocessing Image
# For Resnet
if any('resnet_v1' in name for name in output_tensor_names):
# add imagenet mean - extracted from body-pix source
m = np.array([-123.15, -115.90, -103.06])
x = np.add(x, m)
# For Mobilenet
elif any('MobilenetV1' in name for name in output_tensor_names):
x = (x / 127.5) - 1
else:
print('Unknown Model')
sample_image = x[tf.newaxis, ...]
