Notes on Resizing Images

Sat, 18 Jul 2026 00:00:00 +0000

Notes on Resizing Images

July 2026

Given the influx of AI and readily available tools for things like super resolution (e.g. RealESRGAN), better ways to enhance images when resizing downwards becomes important as well.

The broad idea we should use looks like:

 1orig_w, orig_h = img.size
 2new_h = round(TARGET_WIDTH * orig_h / orig_w)
 3
 4# Gaussian blur pre-filter for anti-aliasing
 5blurred = img.filter(ImageFilter.GaussianBlur(radius=0.5))
 6
 7# One-step Lanczos downscale
 8resized = blurred.resize((TARGET_WIDTH, new_h), Image.LANCZOS)
 9
10output_path = OUTPUT_DIR / f"page_{page_num:03d}.png"
11resized.save(output_path, "PNG", optimize=True)

Where setting something like radius being related to the size of the image scaled. For example, if the resulting image is half the size log the original image, a radius=0.5 is probably appropriate. Whereas if it is 1/5 of the size maybe radius of 1 to 1.5 is better. If you’re after an equation, radius=math.log(scale) is pretty close to the target.

OpenCV 5 Python Bindings Is Here

Wed, 01 Jul 2026 00:00:00 +0000

OpenCV 5 Python Bindings Is Here

July 2026

OpenCV 5 for Python has finally been released to pypi and comes with QoL for running large models through their pipeline. One interesting way to use this pipeline is to use SAM model with LaMa together in a single code base

1sam_result = sam_segment(img)
2if sam_result is None:
3 print(" SAM failed, aborting")
4 return
5mask, point_coords = sam_result
6
7inpainted = lama_inpaint(img, mask)

Where each of the functions are:

 1
 2def sam_segment(img, point_coords=None):
 3 """Segment object using SAM2.1 with point prompts."""
 4 encoder_path = MODELS_DIR / "sam_encoder_v2.onnx"
 5 decoder_path = MODELS_DIR / "sam_decoder_v2.onnx"
 6 if not encoder_path.exists() or not decoder_path.exists():
 7 print(f" [!] SAM models not found in {MODELS_DIR}")
 8 return None
 9
10 t0 = time.time()
11 h, w = img.shape[:2]
12
13 scale = SAM_IMAGE_SIZE / max(h, w)
14 new_h, new_w = int(h * scale), int(w * scale)
15 resized = cv.resize(img, (new_w, new_h), interpolation=cv.INTER_LINEAR)
16 padded = np.zeros((SAM_IMAGE_SIZE, SAM_IMAGE_SIZE, 3), dtype=np.uint8)
17 padded[:new_h, :new_w] = resized
18
19 pixel_values = padded.astype(np.float32) / 255.0
20 pixel_values = (pixel_values - [0.485, 0.456, 0.406]) / [0.229, 0.224, 0.225]
21 pixel_values = pixel_values.transpose(2, 0, 1)[np.newaxis]
22
23 encoder = cv.dnn.readNetFromONNX(str(encoder_path), engine=cv.dnn.ENGINE_NEW)
24 encoder.setInput(pixel_values, "image")
25 image_embed = encoder.forward("image_embed")
26 high_res_0 = encoder.forward("high_res_feats_0")
27 high_res_1 = encoder.forward("high_res_feats_1")
28
29 if point_coords is None:
30 point_coords = [(w // 2, h // 2)]
31 point_labels = [1] * len(point_coords)
32 print(f" Point prompts: {point_coords}")
33
34 scale_x, scale_y = new_w / w, new_h / h
35 pts = np.array([[[int(x * scale_x), int(y * scale_y)] for x, y in point_coords]], dtype=np.float32)
36 labels = np.array([point_labels], dtype=np.float32)
37
38 mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)
39 has_mask_input = np.array([0.0], dtype=np.float32)
40
41 decoder = cv.dnn.readNetFromONNX(str(decoder_path), engine=cv.dnn.ENGINE_AUTO)
42 decoder.setInput(image_embed, "image_embed")
43 decoder.setInput(high_res_0, "high_res_feats_0")
44 decoder.setInput(high_res_1, "high_res_feats_1")
45 decoder.setInput(pts, "point_coords")
46 decoder.setInput(labels, "point_labels")
47 decoder.setInput(mask_input, "mask_input")
48 decoder.setInput(has_mask_input, "has_mask_input")
49
50 masks = decoder.forward("masks")
51 iou_predictions = decoder.forward("iou_predictions")
52
53 best_idx, best_score = 0, -1
54 for i in range(masks.shape[1]):
55 sig = 1.0 / (1.0 + np.exp(-masks[0, i]))
56 area_ratio = sig.mean()
57 if 0.02 < area_ratio < 0.80:
58 score = iou_predictions[0, i]
59 if score > best_score:
60 best_score = score
61 best_idx = i
62 if best_score < 0:
63 for i in range(masks.shape[1]):
64 sig = 1.0 / (1.0 + np.exp(-masks[0, i]))
65 area_ratio = sig.mean()
66 if area_ratio < 0.95:
67 score = iou_predictions[0, i]
68 if score > best_score:
69 best_score = score
70 best_idx = i
71 if best_score < 0:
72 best_idx = int(np.argmax(iou_predictions[0]))
73
74 mask_logits = masks[0, best_idx]
75 mask = (1.0 / (1.0 + np.exp(-mask_logits)) > 0.5).astype(np.uint8) * 255
76 mask = cv.resize(mask, (w, h), interpolation=cv.INTER_NEAREST)
77
78 kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE, (15, 15))
79 mask = cv.dilate(mask, kernel, iterations=3)
80
81 elapsed = time.time() - t0
82 coverage = (mask > 127).mean() * 100
83 print(f" SAM: IoU={iou_predictions[0, best_idx]:.3f}, coverage={coverage:.1f}% [{elapsed:.2f}s]")
84 return mask, point_coords

 1
 2def lama_inpaint(img, mask):
 3 """Remove object using LaMa inpainting model."""
 4 model_path = MODELS_DIR / "lama.onnx"
 5 if not model_path.exists():
 6 print(f" [!] LaMa model not found: {model_path}")
 7 return None
 8
 9 t0 = time.time()
10 h, w = img.shape[:2]
11
12 resized_img = cv.resize(img, (LAMA_MODEL_SIZE, LAMA_MODEL_SIZE))
13 resized_mask = cv.resize(mask, (LAMA_MODEL_SIZE, LAMA_MODEL_SIZE))
14 _, resized_mask = cv.threshold(resized_mask, 127, 255, cv.THRESH_BINARY)
15
16 image_blob = cv.dnn.blobFromImage(resized_img, 1 / 255.0, (LAMA_MODEL_SIZE, LAMA_MODEL_SIZE))
17 mask_blob = cv.dnn.blobFromImage(resized_mask, scalefactor=1.0, size=(LAMA_MODEL_SIZE, LAMA_MODEL_SIZE), mean=(0,), swapRB=False, crop=False)
18 mask_blob = (mask_blob > 0).astype(np.float32)
19
20 net = cv.dnn.readNetFromONNX(str(model_path), engine=cv.dnn.ENGINE_AUTO)
21 net.setInput(image_blob, "image")
22 net.setInput(mask_blob, "mask")
23 output = net.forward()
24
25 result = output[0]
26 result = np.transpose(result, (1, 2, 0))
27 result = np.clip(result, 0, 255).astype(np.uint8)
28 result = cv.resize(result, (w, h))
29
30 elapsed = time.time() - t0
31 print(f" LaMa: inpainting complete [{elapsed:.2f}s]")
32 return result

OpenCV on NoRaincheck

Notes on Resizing Images