Upload app.py

app.py

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+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()