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import gradio as gr
import tensorflow as tf
import numpy as np
import cv2
from huggingface_hub import from_pretrained_keras
def resize_image(img_in,input_height,input_width):
return cv2.resize( img_in, ( input_width,input_height) ,interpolation=cv2.INTER_NEAREST)
def do_prediction(img):
model = from_pretrained_keras("vahidrezanezhad/sbb_binarization")
img_height_model=model.layers[len(model.layers)-1].output_shape[1]
img_width_model=model.layers[len(model.layers)-1].output_shape[2]
n_classes=model.layers[len(model.layers)-1].output_shape[3]
print(img_height_model, img_width_model, n_classes,'didi')
kernel = np.ones((5,5),np.uint8)
margin = int(0.1 * img_width_model)
width_mid = img_width_model - 2 * margin
height_mid = img_height_model - 2 * margin
img = img / float(255.0)
img_h = img.shape[0]
img_w = img.shape[1]
prediction_true = np.zeros((img_h, img_w, 3))
mask_true = np.zeros((img_h, img_w))
nxf = img_w / float(width_mid)
nyf = img_h / float(height_mid)
if nxf > int(nxf):
nxf = int(nxf) + 1
else:
nxf = int(nxf)
if nyf > int(nyf):
nyf = int(nyf) + 1
else:
nyf = int(nyf)
for i in range(nxf):
for j in range(nyf):
if i == 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
elif i > 0:
index_x_d = i * width_mid
index_x_u = index_x_d + img_width_model
if j == 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
elif j > 0:
index_y_d = j * height_mid
index_y_u = index_y_d + img_height_model
if index_x_u > img_w:
index_x_u = img_w
index_x_d = img_w - img_width_model
if index_y_u > img_h:
index_y_u = img_h
index_y_d = img_h - img_height_model
img_patch = img[index_y_d:index_y_u, index_x_d:index_x_u, :]
label_p_pred = model.predict(
img_patch.reshape(1, img_patch.shape[0], img_patch.shape[1], img_patch.shape[2]), verbose=0)
seg = np.argmax(label_p_pred, axis=2)
seg_color = np.repeat(seg[:, :, np.newaxis], 3, axis=2)
if i==0 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
seg = seg[0:seg.shape[0] - margin, 0:seg.shape[1] - margin]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + 0:index_x_u - margin,
:] = seg_color
elif i==nxf-1 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - 0, :]
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - 0]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - 0,
:] = seg_color
elif i==0 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, 0:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - 0, 0:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + 0:index_x_u - margin,
:] = seg_color
elif i==nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - 0]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - 0,
:] = seg_color
elif i==0 and j!=0 and j!=nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - margin, 0:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - margin, 0:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + 0:index_x_u - margin,
:] = seg_color
elif i==nxf-1 and j!=0 and j!=nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - 0, :]
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - 0]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - 0,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==0:
seg_color = seg_color[0:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
seg = seg[0:seg.shape[0] - margin, margin:seg.shape[1] - margin]
mask_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + 0:index_y_u - margin, index_x_d + margin:index_x_u - margin,
:] = seg_color
elif i!=0 and i!=nxf-1 and j==nyf-1:
seg_color = seg_color[margin:seg_color.shape[0] - 0, margin:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - 0, margin:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - 0, index_x_d + margin:index_x_u - margin,
:] = seg_color
else:
seg_color = seg_color[margin:seg_color.shape[0] - margin, margin:seg_color.shape[1] - margin, :]
seg = seg[margin:seg.shape[0] - margin, margin:seg.shape[1] - margin]
mask_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin] = seg
prediction_true[index_y_d + margin:index_y_u - margin, index_x_d + margin:index_x_u - margin,
:] = seg_color
prediction_true = prediction_true.astype(np.uint8)
y_predi=cv2.resize( y_predi, ( img.shape[1],img.shape[0]) ,interpolation=cv2.INTER_NEAREST)
#return y_predi
print(y_predi.shape, np.unique(y_predi))
'''
img = img / float(255.0)
image = resize_image(image, 224,448)
prediction = model.predict(image.reshape(1,224,448,image.shape[2]))
prediction = tf.squeeze(tf.round(prediction))
prediction = np.argmax(prediction,axis=2)
prediction = np.repeat(prediction[:, :, np.newaxis]*255, 3, axis=2)
print(prediction.shape)
'''
return y_predi
iface = gr.Interface(fn=do_prediction, inputs=gr.Image(), outputs=gr.Image())
iface.launch()
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