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Upload LoRa_vertical_slice.ipynb
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LoRa_vertical_slice.ipynb
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{"cells":[{"cell_type":"code","source":["import os\n","import io\n","import random\n","from google.colab import files\n","from PIL import Image\n","import cv2\n","import numpy as np\n","\n","# ────────────────────────────────────────────────\n","# Number of frames to produce\n","# ────────────────────────────────────────────────\n","num_frames = 500 #@param {type:\"slider\", min:1, max:500, step:1}\n","\n","# ────────────────────────────────────────────────\n","# Upload your images/videos\n","# ────────────────────────────────────────────────\n","print(\"Upload your jpg, jpeg, png, webp or webm files\")\n","uploaded = files.upload()\n","\n","# Collect images\n","images = []\n","\n","for filename, filedata in uploaded.items():\n"," fname_lower = filename.lower()\n"," if fname_lower.endswith(('.jpg', '.jpeg', '.png', '.webp')):\n"," try:\n"," img = Image.open(io.BytesIO(filedata)).convert('RGB')\n"," images.append(img)\n"," except:\n"," print(f\"Could not open image: {filename}\")\n"," elif fname_lower.endswith('.webm'):\n"," try:\n"," cap = cv2.VideoCapture(filename)\n"," ret, frame = cap.read()\n"," cap.release()\n"," if ret:\n"," frame_rgb = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)\n"," img = Image.fromarray(frame_rgb)\n"," images.append(img)\n"," else:\n"," print(f\"Could not read video frame: {filename}\")\n"," except:\n"," print(f\"Error processing video: {filename}\")\n","\n","if not images:\n"," print(\"No valid images were loaded. Please upload jpg/jpeg/png/webp/webm files.\")\n","else:\n"," print(f\"Loaded {len(images)} images\")\n","\n"," # ────────────────────────────────────────────────\n"," # Settings\n"," # ────────────────────────────────────────────────\n"," FRAME_SIZE = 1024\n"," BORDER_PX = 14\n"," INNER_SIZE = FRAME_SIZE - 2 * BORDER_PX # 996\n"," NUM_SLICES = 6\n"," SLICE_WIDTH = INNER_SIZE // NUM_SLICES # 83\n"," BORDER_COLOR = (24, 24, 24) # #181818\n","\n"," # ────────────────────────────────────────────────\n"," # Extract multiple vertical slices from each image\n"," # ────────────────────────────────────────────────\n"," all_slices = []\n"," target_w = SLICE_WIDTH\n"," target_h = INNER_SIZE\n","\n"," for photo in images:\n"," w, h = photo.size\n"," if h == 0 or w == 0:\n"," continue\n","\n"," # Resize so height matches target_h (full inner height)\n"," scale = target_h / h\n"," new_w = int(w * scale + 0.5) # better rounding\n"," new_h = target_h\n","\n"," if new_w < 1:\n"," continue\n","\n"," resized = photo.resize((new_w, new_h), Image.LANCZOS)\n","\n"," # How many full slices can we get?\n"," if new_w < target_w:\n"," # Too narrow → center-pad to one slice\n"," slice_img = Image.new('RGB', (target_w, target_h), BORDER_COLOR)\n"," paste_x = (target_w - new_w) // 2\n"," slice_img.paste(resized, (paste_x, 0))\n"," all_slices.append(slice_img)\n"," else:\n"," # Multiple slices possible\n"," num_possible = new_w // target_w\n"," if num_possible < 1:\n"," num_possible = 1\n","\n"," total_width_used = num_possible * target_w\n"," start_left = (new_w - total_width_used) // 2 # center the used area\n","\n"," for i in range(num_possible):\n"," left = start_left + i * target_w\n"," crop = resized.crop((left, 0, left + target_w, target_h))\n"," all_slices.append(crop)\n","\n"," print(f\"Extracted {len(all_slices)} vertical slices from {len(images)} images\")\n","\n"," if len(all_slices) == 0:\n"," print(\"No usable slices could be extracted.\")\n"," else:\n"," output_dir = \"vertical_slice_frames\"\n"," os.makedirs(output_dir, exist_ok=True)\n","\n"," # ────────────────────────────────────────────────\n"," # Randomize selection for each frame independently\n"," # ────────────────────────────────────────────────\n"," frame_idx = 0\n","\n"," while frame_idx < num_frames:\n"," # For each frame: randomly sample 12 slices (with replacement = possible duplicates)\n"," # This gives maximum variety across frames even with few source images/slices\n"," group = random.sample(all_slices * 3, NUM_SLICES) # ×3 to reduce chance of heavy repetition\n","\n"," # Shuffle them again so order is random\n"," random.shuffle(group)\n","\n"," # Build canvas\n"," canvas = Image.new('RGB', (INNER_SIZE, INNER_SIZE), (0, 0, 0))\n","\n"," for col in range(NUM_SLICES):\n"," crop = group[col]\n"," x = col * SLICE_WIDTH\n"," canvas.paste(crop, (x, 0))\n","\n"," # Add border\n"," final = Image.new('RGB', (FRAME_SIZE, FRAME_SIZE), BORDER_COLOR)\n"," final.paste(canvas, (BORDER_PX, BORDER_PX))\n","\n"," # Save\n"," frame_idx += 1\n"," out_path = os.path.join(output_dir, f\"frame_{frame_idx:03d}.jpg\")\n"," final.save(out_path, \"JPEG\", quality=92)\n"," print(f\"Saved: {out_path}\")\n","\n"," print(f\"\\nDone. Created {frame_idx} randomized frames in folder: /{output_dir}\")\n","\n"," # Zip & download\n"," zip_name = \"vertical_frames.zip\"\n"," !zip -r -q {zip_name} {output_dir}\n"," files.download(zip_name)"],"metadata":{"id":"pZyo021uI47v","colab":{"base_uri":"https://localhost:8080/","height":1000},"executionInfo":{"status":"ok","timestamp":1772755212002,"user_tz":-60,"elapsed":183418,"user":{"displayName":"No Name","userId":"10578412414437288386"}},"outputId":"ea7f7064-e405-4bba-de05-5983f2d1d0f8"},"execution_count":1,"outputs":[{"output_type":"stream","name":"stdout","text":["Upload your jpg, jpeg, png, webp or webm files\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n"," <input type=\"file\" id=\"files-439b5bb1-fc14-4a2f-b13f-83969e6499dd\" name=\"files[]\" multiple disabled\n"," style=\"border:none\" />\n"," <output id=\"result-439b5bb1-fc14-4a2f-b13f-83969e6499dd\">\n"," Upload widget is only available when the cell has been executed in the\n"," current browser session. Please rerun this cell to enable.\n"," </output>\n"," <script>// Copyright 2017 Google LLC\n","//\n","// Licensed under the Apache License, Version 2.0 (the \"License\");\n","// you may not use this file except in compliance with the License.\n","// You may obtain a copy of the License at\n","//\n","// http://www.apache.org/licenses/LICENSE-2.0\n","//\n","// Unless required by applicable law or agreed to in writing, software\n","// distributed under the License is distributed on an \"AS IS\" BASIS,\n","// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n","// See the License for the specific language governing permissions and\n","// limitations under the License.\n","\n","/**\n"," * @fileoverview Helpers for google.colab Python module.\n"," */\n","(function(scope) {\n","function span(text, styleAttributes = {}) {\n"," const element = document.createElement('span');\n"," element.textContent = text;\n"," for (const key of Object.keys(styleAttributes)) {\n"," element.style[key] = styleAttributes[key];\n"," }\n"," return element;\n","}\n","\n","// Max number of bytes which will be uploaded at a time.\n","const MAX_PAYLOAD_SIZE = 100 * 1024;\n","\n","function _uploadFiles(inputId, outputId) {\n"," const steps = uploadFilesStep(inputId, outputId);\n"," const outputElement = document.getElementById(outputId);\n"," // Cache steps on the outputElement to make it available for the next call\n"," // to uploadFilesContinue from Python.\n"," outputElement.steps = steps;\n","\n"," return _uploadFilesContinue(outputId);\n","}\n","\n","// This is roughly an async generator (not supported in the browser yet),\n","// where there are multiple asynchronous steps and the Python side is going\n","// to poll for completion of each step.\n","// This uses a Promise to block the python side on completion of each step,\n","// then passes the result of the previous step as the input to the next step.\n","function _uploadFilesContinue(outputId) {\n"," const outputElement = document.getElementById(outputId);\n"," const steps = outputElement.steps;\n","\n"," const next = steps.next(outputElement.lastPromiseValue);\n"," return Promise.resolve(next.value.promise).then((value) => {\n"," // Cache the last promise value to make it available to the next\n"," // step of the generator.\n"," outputElement.lastPromiseValue = value;\n"," return next.value.response;\n"," });\n","}\n","\n","/**\n"," * Generator function which is called between each async step of the upload\n"," * process.\n"," * @param {string} inputId Element ID of the input file picker element.\n"," * @param {string} outputId Element ID of the output display.\n"," * @return {!Iterable<!Object>} Iterable of next steps.\n"," */\n","function* uploadFilesStep(inputId, outputId) {\n"," const inputElement = document.getElementById(inputId);\n"," inputElement.disabled = false;\n","\n"," const outputElement = document.getElementById(outputId);\n"," outputElement.innerHTML = '';\n","\n"," const pickedPromise = new Promise((resolve) => {\n"," inputElement.addEventListener('change', (e) => {\n"," resolve(e.target.files);\n"," });\n"," });\n","\n"," const cancel = document.createElement('button');\n"," inputElement.parentElement.appendChild(cancel);\n"," cancel.textContent = 'Cancel upload';\n"," const cancelPromise = new Promise((resolve) => {\n"," cancel.onclick = () => {\n"," resolve(null);\n"," };\n"," });\n","\n"," // Wait for the user to pick the files.\n"," const files = yield {\n"," promise: Promise.race([pickedPromise, cancelPromise]),\n"," response: {\n"," action: 'starting',\n"," }\n"," };\n","\n"," cancel.remove();\n","\n"," // Disable the input element since further picks are not allowed.\n"," inputElement.disabled = true;\n","\n"," if (!files) {\n"," return {\n"," response: {\n"," action: 'complete',\n"," }\n"," };\n"," }\n","\n"," for (const file of files) {\n"," const li = document.createElement('li');\n"," li.append(span(file.name, {fontWeight: 'bold'}));\n"," li.append(span(\n"," `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n"," `last modified: ${\n"," file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n"," 'n/a'} - `));\n"," const percent = span('0% done');\n"," li.appendChild(percent);\n","\n"," outputElement.appendChild(li);\n","\n"," const fileDataPromise = new Promise((resolve) => {\n"," const reader = new FileReader();\n"," reader.onload = (e) => {\n"," resolve(e.target.result);\n"," };\n"," reader.readAsArrayBuffer(file);\n"," });\n"," // Wait for the data to be ready.\n"," let fileData = yield {\n"," promise: fileDataPromise,\n"," response: {\n"," action: 'continue',\n"," }\n"," };\n","\n"," // Use a chunked sending to avoid message size limits. See b/62115660.\n"," let position = 0;\n"," do {\n"," const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n"," const chunk = new Uint8Array(fileData, position, length);\n"," position += length;\n","\n"," const base64 = btoa(String.fromCharCode.apply(null, chunk));\n"," yield {\n"," response: {\n"," action: 'append',\n"," file: file.name,\n"," data: base64,\n"," },\n"," };\n","\n"," let percentDone = fileData.byteLength === 0 ?\n"," 100 :\n"," Math.round((position / fileData.byteLength) * 100);\n"," percent.textContent = `${percentDone}% done`;\n","\n"," } while (position < fileData.byteLength);\n"," }\n","\n"," // All done.\n"," yield {\n"," response: {\n"," action: 'complete',\n"," }\n"," };\n","}\n","\n","scope.google = scope.google || {};\n","scope.google.colab = scope.google.colab || {};\n","scope.google.colab._files = {\n"," _uploadFiles,\n"," _uploadFilesContinue,\n","};\n","})(self);\n","</script> 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Created 500 randomized frames in folder: /vertical_slice_frames\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["\n"," async function download(id, filename, size) {\n"," if (!google.colab.kernel.accessAllowed) {\n"," return;\n"," }\n"," const div = document.createElement('div');\n"," const label = document.createElement('label');\n"," label.textContent = `Downloading \"${filename}\": `;\n"," div.appendChild(label);\n"," const progress = document.createElement('progress');\n"," progress.max = size;\n"," div.appendChild(progress);\n"," document.body.appendChild(div);\n","\n"," const buffers = [];\n"," let downloaded = 0;\n","\n"," const channel = await google.colab.kernel.comms.open(id);\n"," // Send a message to notify the kernel that we're ready.\n"," channel.send({})\n","\n"," for await (const message of channel.messages) {\n"," // Send a message to notify the kernel that we're ready.\n"," channel.send({})\n"," if (message.buffers) {\n"," for (const buffer of message.buffers) {\n"," buffers.push(buffer);\n"," downloaded += buffer.byteLength;\n"," progress.value = downloaded;\n"," }\n"," }\n"," }\n"," const blob = new Blob(buffers, {type: 'application/binary'});\n"," const a = document.createElement('a');\n"," a.href = window.URL.createObjectURL(blob);\n"," a.download = filename;\n"," div.appendChild(a);\n"," a.click();\n"," div.remove();\n"," }\n"," "]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.Javascript object>"],"application/javascript":["download(\"download_75d0ba87-c4e8-480b-a7e3-23d0829cd70e\", \"vertical_frames.zip\", 82575075)"]},"metadata":{}}]}],"metadata":{"colab":{"provenance":[{"file_id":"11rioWNojWzoPp-O4wZK97yjublkR9GtQ","timestamp":1772750616448},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1772741185417},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1763646205520},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/Drive to WebP.ipynb","timestamp":1760993725927},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1760450712160},{"file_id":"https://huggingface.co/datasets/codeShare/lora-training-data/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1756712618300},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1747490904984},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1740037333374},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1736477078136},{"file_id":"https://huggingface.co/codeShare/JupyterNotebooks/blob/main/YT-playlist-to-mp3.ipynb","timestamp":1725365086834}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0}
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