Tensorflow对象检测API:如何提高图像的检测分数

Question

使用Tensorflow API检测API提出了较低的检测分数问题，不知道如何提高检测分数，而使用较低的检测分数获得IndexError:列出超出范围的索引

需要关于如何删除错误的建议

image_path = "C:/Users/Documents/pdf2txt/invoice.jpg"
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in [
'num_detections', 'detection_boxes', 'detection_scores',
'detection_classes', 'detection_masks'
]:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(
tensor_name)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(
detection_masks, detection_boxes, image.shape[0], image.shape[1])
detection_masks_reframed = tf.cast(
tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(
detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,
feed_dict={image_tensor: np.expand_dims(image, 0)})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict[
'detection_classes'][0].astype(np.uint8)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
print(output_dict['detection_scores'])
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict

对于TEST_image_PATHS:中的image_path

image = Image.open(image_path)
image_np = load_image_into_numpy_array(image)
image_np_expanded = np.expand_dims(image_np, axis=0)
output_dict = run_inference_for_single_image(image_np, detection_graph)
outImage = Image.fromarray(image_np)

firstResult = output_dict['detection_boxes'][0]
firstArray = []
score = output_dict['detection_scores'][0]
print(score)
# if score > float(0.85):
for coords in firstResult:
realCoord = coords*1024
firstArray.append(realCoord)
firstImage = image.crop((firstArray[1], firstArray[0],firstArray[3],firstArray[2]))
outputClass = output_di ct['detection_classes'][0]
parameter =  CLASSES[outputClass - 1]
coordText = str(firstArray[1]) + " " + str(firstArray[0]) + " " + str(firstArray[3]) + " " +str(firstArray[2]) + " " + parameter + 'xout1.tif'
coordsFile.write(coordText + "n")
firstImage.save(r'C:/Users/neerajjha/Documents/pdf2txt/object_detection/Results/' + parameter + 'xout1.tif')
print(coordsFile)

输出：

Traceback (most recent call last):
File "c:/Users/Documents/pdf2txt/server_detection.py", line 260, in <module>
firstImage = image.crop((firstArray[1], firstArray[0],firstArray[3],firstArray[2]))
IndexError: list index out of range

请建议！！

Answer 1

我认为问题出在这段代码中：

for coords in firstResult:
realCoord = coords*1024
firstArray.append(realCoord)
firstImage = image.crop((firstArray[1], firstArray[0],firstArray[3],firstArray[2]))

FirstResult应包含模型检测到的边界框的4个坐标。在image.crop函数中使用firstArray之前，可以尝试将最后一行从for loop中移出，以便将所有4个值都添加到firstArray中吗？