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17868be | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | import numpy as np
import cv2
import gradio as gr
print("loading models.....")
net = cv2.dnn.readNetFromCaffe('colorization_deploy_v2.prototxt','colorization_release_v2.caffemodel')
pts = np.load('pts_in_hull.npy')
class8 = net.getLayerId("class8_ab")
conv8 = net.getLayerId("conv8_313_rh")
pts = pts.transpose().reshape(2,313,1,1)
net.getLayer(class8).blobs = [pts.astype("float32")]
net.getLayer(conv8).blobs = [np.full([1,313],2.606,dtype='float32')]
def colorize_image(image):
# Convert the PIL image to OpenCV format
image = np.array(image)
image = cv2.cvtColor(image, cv2.COLOR_RGB2BGR)
scaled = image.astype("float32")/255.0
lab = cv2.cvtColor(scaled, cv2.COLOR_BGR2LAB)
resized = cv2.resize(lab, (224, 224))
L = cv2.split(resized)[0]
L -= 50
net.setInput(cv2.dnn.blobFromImage(L))
ab = net.forward()[0, :, :, :].transpose((1, 2, 0))
ab = cv2.resize(ab, (image.shape[1], image.shape[0]))
L = cv2.split(lab)[0]
colorized = np.concatenate((L[:, :, np.newaxis], ab), axis=2)
colorized = cv2.cvtColor(colorized, cv2.COLOR_LAB2RGB)
colorized = np.clip(colorized, 0, 1)
colorized = (255 * colorized).astype("uint8")
return colorized
demo = gr.Interface(
colorize_image,
gr.Image(type="pil",label='Upload a black and white image'),
"image",
title="Image Colorization",
examples = ["Landscape.jpg","nnl.jpg","bw.jpg"]
)
if __name__ == "__main__":
demo.launch()
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