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Use opencv reisze method for preprocessing of image in darknet #4883

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Feb 25, 2020
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62 changes: 21 additions & 41 deletions python/tvm/relay/testing/darknet.py
Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@
# KIND, either express or implied. See the License for the
# specific language governing permissions and limitations
# under the License.
# pylint: disable=invalid-name, unused-variable, unused-argument, no-init, unpacking-non-sequence
# pylint: disable=invalid-name, unused-variable, unused-argument, no-init
"""
Compile DarkNet Models
====================
Expand All @@ -23,68 +23,49 @@
These are utility functions used for testing and tutorial file.
"""
from __future__ import division
import math
import numpy as np
from cffi import FFI
import cv2

def _resize_image(img, w_in, h_in):
"""Resize the image to the given height and width."""
imc, imh, imw = img.shape
h_in = int(h_in)
w_in = int(w_in)
part = np.zeros((imc, imh, w_in))
resized = np.zeros((imc, h_in, w_in))
w_scale = (imw - 1) / (w_in - 1)
h_scale = (imh - 1) / (h_in - 1)
for k in range(imc):
for j in range(imh):
for c in range(w_in):
if c == w_in - 1 or imw == 1:
part[k][j][c] = img[k][j][imw - 1]
else:
fdx, idx = math.modf(c * w_scale)
part[k][j][c] = (1 - fdx) * img[k][j][int(idx)] + \
fdx * img[k][j][int(idx) + 1]
for k in range(imc):
for j in range(h_in):
fdy, idy = math.modf(j * h_scale)
for c in range(w_in):
resized[k][j][c] = (1 - fdy)*part[k][int(idy)][c]
if (j == h_in - 1) or (imh == 1):
continue
for c in range(w_in):
resized[k][j][c] += fdy * part[k][int(idy) + 1][c]
return resized

def load_image_color(test_image):
"""To load the image using opencv api and do preprocessing."""
imagex = cv2.imread(test_image)
imagex = cv2.cvtColor(imagex, cv2.COLOR_BGR2RGB)
imagex = np.array(imagex)
def convert_image(image):
"""Convert the image with numpy."""
imagex = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
imagex = np.array(image)
imagex = imagex.transpose((2, 0, 1))
imagex = np.divide(imagex, 255.0)
imagex = np.flip(imagex, 0)
return imagex

def load_image_color(test_image):
"""To load the image using opencv api and do preprocessing."""
imagex = cv2.imread(test_image)
return convert_image(imagex)

def _letterbox_image(img, w_in, h_in):
"""To get the image in boxed format."""
imc, imh, imw = img.shape
imh, imw, imc = img.shape
if (w_in / imw) < (h_in / imh):
new_w = w_in
new_h = imh * w_in // imw
else:
new_h = h_in
new_w = imw * h_in // imh
resized = _resize_image(img, new_w, new_h)
dim = (new_w, new_h)
# Default interpolation method is INTER_LINEAR
# Other methods are INTER_AREA, INTER_NEAREST, INTER_CUBIC and INTER_LANCZOS4
# For more information see:
# https://docs.opencv.org/2.4/modules/imgproc/doc/geometric_transformations.html#resize
resized = cv2.resize(src=img, dsize=dim, interpolation=cv2.INTER_CUBIC)
resized = convert_image(resized)
boxed = np.full((imc, h_in, w_in), 0.5, dtype=float)
_, resizedh, resizedw = resized.shape
boxed[:, int((h_in - new_h) / 2)
:int((h_in - new_h) / 2) + resizedh, int((w_in - new_w) / 2)
:int((w_in - new_w) / 2) + resizedw] = resized
return boxed

def load_image(image, resize_width, resize_height):
def load_image(img, resize_width, resize_height):
"""Load the image and convert to the darknet model format.
The image processing of darknet is different from normal.
Parameters
Expand All @@ -103,9 +84,8 @@ def load_image(image, resize_width, resize_height):
img : Float array
Array of processed image
"""

img = load_image_color(image)
return _letterbox_image(img, resize_width, resize_height)
imagex = cv2.imread(img)
return _letterbox_image(imagex, resize_width, resize_height)

class LAYERTYPE(object):
"""Darknet LAYERTYPE Class constant."""
Expand Down