added new title

.history/app_20231208153741.py

@@ -0,0 +1,77 @@
+import gradio as gr
+from PIL import Image
+from torchvision import transforms
+import torch
+import random
+import os
+from models.structure.model import SketchKeras
+from safetensors.torch import load_model
+import cv2
+import numpy as np
+
+
+path_to_weights = os.path.join(
+    os.path.dirname(__file__), "models/weights/sketch_keras.st"
+)
+model = SketchKeras()
+load_model(model, path_to_weights)
+model.eval()
+
+
+def preprocess(img):
+    h, w, c = img.shape
+    blurred = cv2.GaussianBlur(img, (0, 0), 3)
+    highpass = img.astype(int) - blurred.astype(int)
+    highpass = highpass.astype(float) / 128.0
+    highpass /= np.max(highpass)
+
+    ret = np.zeros((512, 512, 3), dtype=float)
+    ret[0:h, 0:w, 0:c] = highpass
+    return ret
+
+
+def postprocess(pred, thresh=0.18, smooth=False):
+    assert thresh <= 1.0 and thresh >= 0.0
+
+    pred = np.amax(pred, 0)
+    pred[pred < thresh] = 0
+    pred = 1 - pred
+    pred *= 255
+    pred = np.clip(pred, 0, 255).astype(np.uint8)
+    if smooth:
+        pred = cv2.medianBlur(pred, 3)
+    return pred
+
+
+def output_sketch(img):
+    # resize
+    height, width = float(img.shape[0]), float(img.shape[1])
+    if width > height:
+        new_width, new_height = (512, int(512 / width * height))
+    else:
+        new_width, new_height = (int(512 / height * width), 512)
+    img = cv2.resize(img, (new_width, new_height))
+
+    img = preprocess(img)
+    x = img.reshape(1, *img.shape).transpose(3, 0, 1, 2)
+    x = torch.tensor(x).float()
+
+    with torch.no_grad():
+        pred = model(x)
+
+    pred = pred.squeeze()
+
+    # postprocess
+    output = pred.cpu().detach().numpy()
+    output = postprocess(output, thresh=0.1, smooth=False)
+    output = output[:new_height, :new_width]
+
+    return output
+
+
+gr.Interface(
+    title="Turn Any Image Into a Sketch",
+    fn=output_sketch,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(type="numpy"),
+).launch()

.history/app_20231208160008.py

@@ -0,0 +1,77 @@
+import gradio as gr
+from PIL import Image
+from torchvision import transforms
+import torch
+import random
+import os
+from models.structure.model import SketchKeras
+from safetensors.torch import load_model
+import cv2
+import numpy as np
+
+
+path_to_weights = os.path.join(
+    os.path.dirname(__file__), "models/weights/sketch_keras.st"
+)
+model = SketchKeras()
+load_model(model, path_to_weights)
+model.eval()
+
+
+def preprocess(img):
+    h, w, c = img.shape
+    blurred = cv2.GaussianBlur(img, (0, 0), 3)
+    highpass = img.astype(int) - blurred.astype(int)
+    highpass = highpass.astype(float) / 128.0
+    highpass /= np.max(highpass)
+
+    ret = np.zeros((512, 512, 3), dtype=float)
+    ret[0:h, 0:w, 0:c] = highpass
+    return ret
+
+
+def postprocess(pred, thresh=0.18, smooth=False):
+    assert thresh <= 1.0 and thresh >= 0.0
+
+    pred = np.amax(pred, 0)
+    pred[pred < thresh] = 0
+    pred = 1 - pred
+    pred *= 255
+    pred = np.clip(pred, 0, 255).astype(np.uint8)
+    if smooth:
+        pred = cv2.medianBlur(pred, 3)
+    return pred
+
+
+def output_sketch(img):
+    # resize
+    height, width = float(img.shape[0]), float(img.shape[1])
+    if width > height:
+        new_width, new_height = (512, int(512 / width * height))
+    else:
+        new_width, new_height = (int(512 / height * width), 512)
+    img = cv2.resize(img, (new_width, new_height))
+
+    img = preprocess(img)
+    x = img.reshape(1, *img.shape).transpose(3, 0, 1, 2)
+    x = torch.tensor(x).float()
+
+    with torch.no_grad():
+        pred = model(x)
+
+    pred = pred.squeeze()
+
+    # postprocess
+    output = pred.cpu().detach().numpy()
+    output = postprocess(output, thresh=0.1, smooth=False)
+    output = output[:new_height, :new_width]
+
+    return output
+
+
+gr.Interface(
+    title="Turn Any Image Into a Sketch with SketchKeras",
+    fn=output_sketch,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(type="numpy"),
+).launch()

.history/app_20231208160016.py

@@ -0,0 +1,77 @@
+import gradio as gr
+from PIL import Image
+from torchvision import transforms
+import torch
+import random
+import os
+from models.structure.model import SketchKeras
+from safetensors.torch import load_model
+import cv2
+import numpy as np
+
+
+path_to_weights = os.path.join(
+    os.path.dirname(__file__), "models/weights/sketch_keras.st"
+)
+model = SketchKeras()
+load_model(model, path_to_weights)
+model.eval()
+
+
+def preprocess(img):
+    h, w, c = img.shape
+    blurred = cv2.GaussianBlur(img, (0, 0), 3)
+    highpass = img.astype(int) - blurred.astype(int)
+    highpass = highpass.astype(float) / 128.0
+    highpass /= np.max(highpass)
+
+    ret = np.zeros((512, 512, 3), dtype=float)
+    ret[0:h, 0:w, 0:c] = highpass
+    return ret
+
+
+def postprocess(pred, thresh=0.18, smooth=False):
+    assert thresh <= 1.0 and thresh >= 0.0
+
+    pred = np.amax(pred, 0)
+    pred[pred < thresh] = 0
+    pred = 1 - pred
+    pred *= 255
+    pred = np.clip(pred, 0, 255).astype(np.uint8)
+    if smooth:
+        pred = cv2.medianBlur(pred, 3)
+    return pred
+
+
+def output_sketch(img):
+    # resize
+    height, width = float(img.shape[0]), float(img.shape[1])
+    if width > height:
+        new_width, new_height = (512, int(512 / width * height))
+    else:
+        new_width, new_height = (int(512 / height * width), 512)
+    img = cv2.resize(img, (new_width, new_height))
+
+    img = preprocess(img)
+    x = img.reshape(1, *img.shape).transpose(3, 0, 1, 2)
+    x = torch.tensor(x).float()
+
+    with torch.no_grad():
+        pred = model(x)
+
+    pred = pred.squeeze()
+
+    # postprocess
+    output = pred.cpu().detach().numpy()
+    output = postprocess(output, thresh=0.1, smooth=False)
+    output = output[:new_height, :new_width]
+
+    return output
+
+
+gr.Interface(
+    title="Turn Any Image Into a Sketch with This App",
+    fn=output_sketch,
+    inputs=gr.Image(type="numpy"),
+    outputs=gr.Image(type="numpy"),
+).launch()

app.py

@@ -70,7 +70,7 @@ def output_sketch(img):
 
 
 gr.Interface(
-    title="Turn Any Image Into a Sketch",
+    title="Turn Any Image Into a Sketch with This App",
     fn=output_sketch,
     inputs=gr.Image(type="numpy"),
     outputs=gr.Image(type="numpy"),