[英]How to replicate tensorflow image processing using opencv python
嗨,我需要使用python cv2來完成由tensorflow在下面的代碼片段中進行的完全相同的預處理。 請幫忙!
file_name = 'image path'
input_name = 'file_reader'
input_height=299
input_width=299
input_mean=0
input_std=255
file_reader = tf.read_file(file_name, input_name)
image_reader = tf.image.decode_jpeg(file_reader, channels = 3,
name='jpeg_reader')
float_caster = tf.cast(image_reader, tf.float32)
dims_expander = tf.expand_dims(float_caster, 0);
resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
sess = tf.Session()
result = sess.run(normalized)
我將逐行為您的張量流代碼編寫OpenCV代碼:
file_name = 'image path'
input_name = 'file_reader'
input_height=299
input_width=299
input_mean=0
input_std=255
#file_reader = tf.read_file(file_name, input_name)
#image_reader = tf.image.decode_jpeg(file_reader, channels = 3,name='jpeg_reader')
image = cv2.imread(file_name, -1)
#float_caster = tf.cast(image_reader, tf.float32)
float_caster = image.astype(numpy.float32, copy=False)
#dims_expander = tf.expand_dims(float_caster, 0);
#This line just adds another dimension to the image, which is not needed for OpenCV but if you want:
#dims_expander = numpy.expand_dims(float_caster, axis=0)
#resized = tf.image.resize_bilinear(dims_expander, [input_height, input_width])
resized = cv2.resize(float_caster, (input_height,input_width),interpolation=cv2.INTER_LINEAR)
#normalized = tf.divide(tf.subtract(resized, [input_mean]), [input_std])
normalized = resized - input_mean
normalized /= input_std
請記住,OpenCV將圖像讀取為BGR
而tensorflow將圖像讀取為RGB
。 因此,如果要直觀地比較結果,則需要將一個轉換為另一個的順序。
tf.image.resize_bilinear是越野車功能。 因此,在TF和CV2中調整相同圖像的大小后,您將不會獲得完全相同的值。
快速示例:
img_original = np.random.randint(0, 255, (4,4,3)).astype(np.uint8)
img_rescaled = cv2.resize(img_original, (3,3), cv2.INTER_LINEAR)
print(img_original)
array([[[144, 9, 253],
[ 5, 87, 5],
[ 21, 125, 117],
[109, 159, 142]],
[[ 64, 124, 196],
[ 43, 230, 80],
[ 42, 166, 36],
[158, 121, 11]],
[[238, 234, 57],
[ 86, 254, 239],
[149, 133, 161],
[ 96, 245, 99]],
[[128, 7, 134],
[169, 69, 70],
[246, 31, 95],
[143, 1, 58]]], dtype=uint8)
檢查數組如何通過CV2轉換:
print(img_rescaled)
array([[[111, 42, 206],
[ 18, 121, 60],
[102, 149, 117]],
[[137, 189, 132],
[ 80, 196, 129],
[122, 177, 62]],
[[148, 54, 117],
[192, 74, 102],
[151, 42, 71]]], dtype=uint8)
現在,讓我們定義TF會話,看看幕后發生了什么:
x = tf.placeholder(tf.uint8, shape=(None,4,4,3), name='x')
resize_op_TF = tf.image.resize_bilinear(x, (3,3), name='resize_op')
session = tf.InteractiveSession()
img_resized_TF = session.run(resize_op_TF, {x: [img_original]})[0]
print(img_resized_TF)
array([[[144. , 9. , 253. ],
[ 10.333334, 99.66667 , 42.333336],
[ 79.66667 , 147.66667 , 133.66667 ]],
[[122.00001 , 160.66667 , 149.66666 ],
[ 64.111115, 210.33333 , 114.55556 ],
[117.44445 , 159.88889 , 52.77778 ]],
[[164.66666 , 82.66664 , 108.33334 ],
[165.44446 , 108.777756, 123.22222 ],
[156.11111 , 76.55554 , 86.77777 ]]], dtype=float32)
如您所見,使用TF重新縮放的數組中的值和使用CV2重新縮放的數組中的值完全不同。 這是Tensorflow中的常見問題,由於種種原因,開發人員仍未修復它。
聲明:本站的技術帖子網頁,遵循CC BY-SA 4.0協議,如果您需要轉載,請注明本站網址或者原文地址。任何問題請咨詢:yoyou2525@163.com.