def predict(model_path, img_path, partition_num=4):
inputs = "ToFloat:0"
outputs = [
"num_detections:0", "detection_boxes:0", "detection_scores:0",
"detection_classes:0"
]
detector = TFNet(model_path, inputs, outputs)
model = Sequential()
# Transpose TensorFlow NHWC format to Analytics Zoo NCHW format.
model.add(Transpose([(2, 4), (2, 3)]))
model.add(Contiguous())
model.add(detector)
# Select the detection_boxes from the output.
model.add(SelectTable(2))
image_set = ImageSet.read(img_path, sc, partition_num)
transformer = ChainedPreprocessing(
[ImageResize(256, 256),
ImageMatToTensor(),
ImageSetToSample()])
transformed_image_set = image_set.transform(transformer)
output = model.predict_image(transformed_image_set.to_image_frame(),
batch_per_partition=1)
# Print the detection box with the highest score of the first prediction result.
result = output.get_predict().first()
print(result[1][0])
def predict(model_path, img_path, partition_num=4):
inputs = "image_tensor:0"
outputs = ["num_detections:0", "detection_boxes:0",
"detection_scores:0", "detection_classes:0"]
model = TFNet(model_path, inputs, outputs)
image_set = ImageSet.read(img_path, sc, partition_num)
transformer = ChainedPreprocessing([ImageResize(256, 256), ImageMatToTensor(format="NHWC"),
ImageSetToSample()])
transformed_image_set = image_set.transform(transformer)
output = model.predict_image(transformed_image_set.to_image_frame(), batch_per_partition=1)
# Print the detection result of the first image.
result = ImageSet.from_image_frame(output).get_predict().first()
print(result)