[英]Tensorflow Object Detection API : How to improve detection_scores of an image
Using Tensorflow API detection API came up with the lower detection_scores issue, Don't understand how to improve the detection_scores , while Using the lower detection_scores getting IndexError: list index out of range使用 Tensorflow API 检测 API 提出了较低的 detection_scores 问题,不明白如何改进 detection_scores ,而使用较低的 detection_scores 得到IndexError: list index out of range
Need Suggestion on how to remove the Error需要有关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
For image_path in TEST_IMAGE_PATHS: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)
OUTPUT: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
Please Suggest !!请推荐!!
I think the issue is in this piece of code: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]))
The FirstResult should contain the 4 coordinates of the bounding box that the model detects. FirstResult 应包含模型检测到的边界框的 4 个坐标。 Can you try moving the last line out of the
for loop
so that all 4 values are added to the firstArray before you are using it in the image.crop
function?在
image.crop
函数中使用 firstArray 之前,您能否尝试将最后一行移出for loop
以便将所有 4 个值添加到image.crop
?
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