| from fastapi import FastAPI, File, UploadFile |
| from fastapi.responses import StreamingResponse |
| import numpy as np |
| import cv2 |
| import io |
| from PIL import Image |
|
|
| |
| 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')] |
|
|
| |
| app = FastAPI() |
|
|
| def read_image(file): |
| |
| pil_image = Image.open(file) |
|
|
| |
| image_array = np.array(pil_image) |
|
|
| return image_array |
|
|
| def colorize_image(image): |
| |
| 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 |
|
|
| @app.post("/upload/") |
| async def upload(file: UploadFile = File(...)): |
| |
| image_array = read_image(file.file) |
| |
| |
| colorized_image = colorize_image(image_array) |
|
|
| |
| pil_image = Image.fromarray(colorized_image) |
| buf = io.BytesIO() |
| pil_image.save(buf, format="PNG") |
| buf.seek(0) |
|
|
| return StreamingResponse(buf, media_type="image/png") |
|
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