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"""
def pre_process(img: np.array) -> np.array:
# H, W, C -> C, H, W
img = np.transpose(img[:, :, 0:3], (2, 0, 1))
# C, H, W -> 1, C, H, W
img = np.expand_dims(img, axis=0).astype(np.float32)
return img
def post_process(img: np.array) -> np.array:
# 1, C, H, W -> C, H, W
img = np.squeeze(img)
# C, H, W -> H, W, C
img = np.transpose(img, (1, 2, 0))[:, :, ::-1].astype(np.uint8)
return img
def inference(model_path: str, img_array: np.array) -> np.array:
options = onnxruntime.SessionOptions()
options.intra_op_num_threads = 1
options.inter_op_num_threads = 1
ort_session = onnxruntime.InferenceSession(model_path, options)
ort_inputs = {ort_session.get_inputs()[0].name: img_array}
ort_outs = ort_session.run(None, ort_inputs)
return ort_outs[0]
def convert_pil_to_cv2(image):
# pil_image = image.convert("RGB")
open_cv_image = np.array(image)
# RGB to BGR
open_cv_image = open_cv_image[:, :, ::-1].copy()
return open_cv_image
def upscale(image, model):
model_path = f"models/{model}.ort"
img = convert_pil_to_cv2(image)
if img.ndim == 2:
img = cv2.cvtColor(img, cv2.COLOR_GRAY2BGR)
if img.shape[2] == 4:
alpha = img[:, :, 3] # GRAY
alpha = cv2.cvtColor(alpha, cv2.COLOR_GRAY2BGR) # BGR
alpha_output = post_process(inference(model_path, pre_process(alpha))) # BGR
alpha_output = cv2.cvtColor(alpha_output, cv2.COLOR_BGR2GRAY) # GRAY
img = img[:, :, 0:3] # BGR
image_output = post_process(inference(model_path, pre_process(img))) # BGR
image_output = cv2.cvtColor(image_output, cv2.COLOR_BGR2BGRA) # BGRA
image_output[:, :, 3] = alpha_output
elif img.shape[2] == 3:
image_output = post_process(inference(model_path, pre_process(img))) # BGR
return image_output
css = ".output-image, .input-image, .image-preview {height: 480px !important} "
model_choices = ["modelx2", "modelx2 25 JXL", "modelx4", "minecraft_modelx4"]
gr.Interface(
fn=upscale,
inputs=[
gr.Image(type="pil", label="Input Image"),
gr.Radio(
model_choices,
type="value",
value="modelx4",
label="Choose Upscaler",
)
],
# additional_inputs=[
# gr.Radio(
# model_choices,
# type="value",
# value="modelx4",
# label="Choose Upscaler",
# )
# ],
outputs="image",
# title="Image Upscaler PRO ⚡",
# description="Model: [Anchor-based Plain Net for Mobile Image Super-Resolution](https://arxiv.org/abs/2105.09750). Repository: [SR Mobile PyTorch](https://github.com/w11wo/sr_mobile_pytorch)",
description = """
