Create app.py

app.py

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+
+# Import statements
+import numpy as np
+import argparse
+import cv2
+import os
+import gradio as gr
+
+PROTOTXT = "colorization_deploy_v2.prototxt"
+POINTS = "pts_in_hull.npy"
+MODEL = "colorization_release_v2.caffemodel"
+
+
+# Load the Model
+print("Load model")
+net = cv2.dnn.readNetFromCaffe(PROTOTXT, MODEL)
+pts = np.load(POINTS)
+
+# Load centers for ab channel quantization used for rebalancing.
+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 colorizedTheImage(image):
+	# Load the input image
+	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
+
+	print("Colorizing the image")
+	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_LAB2BGR)
+	colorized = np.clip(colorized, 0, 1)
+
+	colorized = (255 * colorized).astype("uint8")
+	return colorized
+# image = cv2.resize(image, (0,0), fx=0.5, fy=0.5) 
+# colorized = cv2.resize(colorized, (0,0), fx=0.5, fy=0.5) 
+# cv2.imshow("Original", image)
+# cv2.imshow("Colorized", colorized)
+# cv2.waitKey(0)
+demo=gr.Interface(fn=colorizedTheImage,
+                  inputs=["image"],
+                  outputs=["image"],
+                  examples=[["einstein.jpg"],["tiger.jpg"]],
+                  title="Black&White To Color Image")