From 04c2f57d248509a25ed2f961de0d0cadeb156c28 Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Sun, 8 Dec 2024 16:22:22 +0900 Subject: [PATCH 01/11] temporal commit 20241208 16:22 --- .../create_path/TuSimple/process_tusimple.py | 157 ++++++++++++++++++ PathDet/create_path/dataset_playground.ipynb | 86 ++++++++++ PathDet/create_path/test_output.png | Bin 0 -> 795980 bytes 3 files changed, 243 insertions(+) create mode 100644 PathDet/create_path/TuSimple/process_tusimple.py create mode 100644 PathDet/create_path/dataset_playground.ipynb create mode 100644 PathDet/create_path/test_output.png diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py new file mode 100644 index 0000000..8802899 --- /dev/null +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -0,0 +1,157 @@ +#! /usr/bin/env python3 + +import argparse +import os +import pathlib +import json +import numpy as np +from PIL import Image, ImageDraw +import sys +import warnings + +# ============================== Dataset structure ============================== # + +root_dir = "TUSimple" +train_dir = "train_set" +test_dir = "test_set" + +train_clip_codes = ["0313", "0531", "0601"] # Train labels are split into 3 dirs +test_file = "test_label.json" # Test file name + +img_width = 1280 +img_height = 720 + +# ============================== Helper functions ============================== # + +def normalizeCoords(lane, width, height): + + return [(x / width, y / height) for x, y in lane] + +def getEgoIndexes(anchors): + + # `anchors` is a list of tuples at form (xi, yi) + # In those labels, lanes are labeled from left to right, so these anchors + # extracted from them are also sorted x-coords in ascending order. + for i in range(len(anchors)): + if (anchors[i][0] >= img_width / 2): + if (i == 0): + return "NO LANES on the LEFT side of frame. Something's wrong!" + left_ego_idx, right_ego_idx = i - 1, i + return (left_ego_idx, right_ego_idx) + + return "NO LANES on the RIGHT side of frame. Something's wrong!" + +def getLaneAnchor(lane): + + (x1, y1) = lane[len(lane) // 2] + (x2, y2) = lane[-1] + if (x1 == x2): + warnings.warn(f"Vertical lane detected: {lane}") + return (x1, None, None) + # print("x1, y1:", x1, y1) + # print("x2, y2:", x2, y2) + a = (y2 - y1) / (x2 - x1) + # print("a:", a) + b = y1 - a * x1 + # print("b:", b) + x0 = (img_height - b) / a + # print("x0:", x0) + + return (x0, b) + +def parseAnnotations(anno_path): + + # Read em raw + with open(anno_path, "r") as f: + read_data = [json.loads(line) for line in f.readlines()] + + # Parse em + anno_data = {} + for item in read_data: + lanes = item["lanes"] + h_samples = item["h_samples"] + raw_file = item["raw_file"] + + lanes_decoupled = [ + [(x, y) for x, y in zip(lane, h_samples) if x != -2] + for lane in lanes + ] + + lane_anchors = [getLaneAnchor(lane) for lane in lanes_decoupled] + + ego_indexes = getEgoIndexes(lane_anchors) + + if (type(ego_indexes) is str): + if (ego_indexes.startswith("NO")): + warnings.warn(f"Error parsing {raw_file}: {ego_indexes}") + continue + + left_ego = lanes_decoupled[ego_indexes[0]] + right_ego = lanes_decoupled[ego_indexes[1]] + + # Parse processed data, all coords normalized + anno_data[raw_file] = { + "lanes": [normalizeCoords(lane, img_width, img_height) for lane in lanes_decoupled], + "ego_indexes": ego_indexes, + "img_size": (img_width, img_height), + } + + # + + return anno_data + +if __name__ == "__main__": + + # ============================== Parsing args ============================== # + + parser = argparse.ArgumentParser( + description = "Process TuSimple dataset - PathDet groundtruth generation" + ) + parser.add_argument( + "--dataset_dir", + type = str, + help = "TuSimple directory (right after extraction)" + ) + parser.add_argument( + "--output_dir", + type = str, + help = "Output directory" + ) + parser.add_argument( + "--test_label_file", + type = str + ) + args = parser.parse_args() + + dataset_dir = pathlib.Path(args.dataset_dir) + output_dir = pathlib.Path(args.output_dir) + output_dir.mkdir(exist_ok = True, parents = True) + test_label_file = args.test_label_file + + # ============================== Parsing annotations ============================== # + + train_label_files = [ + dataset_dir / root_dir / train_dir / f"label_data_{clip_code}.json" + for clip_code in train_clip_codes + ] + + # Make no mistake: the file `TuSimple/test_set/test_tasks_0627.json`` is NOT a label file. + # It is just a template for test submission. Kinda weird they put it in `test_set` while + # the actual label file is in `TuSimple/test_label.json`. + test_label_files = [dataset_dir / root_dir / test_file] + + # Getting list of all json files + # json_files = sorted([f for f in dataset_dir.glob("*.json")]) + + # for json_file in json_files: + # parseAnnotations(json_file) + + # print(train_label_files) + # print(test_label_files) + + first_file = "test_set/clips/0530/1492626760788443246_0/20.jpg" + + test_data = parseAnnotations(test_label_file) + first_test_data = test_data[first_file] + + print(first_test_data) \ No newline at end of file diff --git a/PathDet/create_path/dataset_playground.ipynb b/PathDet/create_path/dataset_playground.ipynb new file mode 100644 index 0000000..a593dcd --- /dev/null +++ b/PathDet/create_path/dataset_playground.ipynb @@ -0,0 +1,86 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pathlib\n", + "import json\n", + "import numpy as np\n", + "from PIL import Image, ImageDraw\n", + "import sys" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Saved output image with lanes overlayed: ./test_output.png\n" + ] + } + ], + "source": [ + "def draw_lanes_on_image(label_file, image_file, output_file):\n", + " # Load the label file\n", + " with open(label_file, 'r') as f:\n", + " label_data = json.load(f)\n", + "\n", + " lanes = label_data['lanes']\n", + " h_samples = label_data['h_samples']\n", + " raw_file = label_data['raw_file']\n", + "\n", + " # Load the original image\n", + " image = Image.open(image_file).convert(\"RGB\")\n", + " draw = ImageDraw.Draw(image)\n", + "\n", + " # Define colors for lanes\n", + " lane_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0)] # Red, Green, Blue, Yellow\n", + "\n", + " # Draw each lane\n", + " for idx, lane in enumerate(lanes):\n", + " points = [(x, y) for x, y in zip(lane, h_samples) if x != -2]\n", + " if points:\n", + " draw.line(points, fill=lane_colors[idx % len(lane_colors)], width=5)\n", + "\n", + " # Save the output image\n", + " image.save(output_file, \"PNG\")\n", + " print(f\"Saved output image with lanes overlayed: {output_file}\")\n", + "\n", + "# Example usage\n", + "label_file = \"/home/tranhuunhathuy/Downloads/tusiumple_1linesample.json\" # Path to the label JSON file\n", + "image_file = \"/home/tranhuunhathuy/Documents/Autoware/pov_datasets/tu_simple/TUSimple/test_set/clips/0530/1492626760788443246_0/20.jpg\" # Path to the input image\n", + "output_file = \"./test_output.png\" # Path to save the output image\n", + "\n", + "draw_lanes_on_image(label_file, image_file, output_file)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "usr", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", 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zeJ-V4z9RWx?kLUm+(<5!bBfn-RVY!Qw=y6XmI75Z8m3Iw*I1{!tB8Qq)rh9Az40g4 z<$7hI2RS#s2xNP97P-CLUbiov`XsP4I1?``x2tb%c*4k;31Wab!A;eenyPMH0R>se zN;i^r+x!)zM&@M3PEDjO?_a-c%gb{8>BS{8c|Pw?W@4u9 z7DMLid3hRF7(h_%YGH^DbDHLma{$@Y2gM|-2az~wvwvg7khIlXi>Vm9#*pG5OMNP~ zIDtSoq6CWL2{YvlYy;lj68$59&W+vL%&QgY~a0&b5 z!tyz_iQNfM^?ojvJip0LGc)(8+05kO{A+?Yd0nNT%9<+2PUNr5Cs|yE1X1`c9TkZr z)jW5f5p|lip_Xg)Cx^**y@UkpI9L~_*JB?0wEioZ}p0wufbZAhBJ#HF|Xk@!`5 z6(v(SY{%6T(wf=2USjVt$0fbSC_eZ65XeyBkf4~fu{%a5039=YSM}i-yO?%}%n>z& zh0By3Kl*og2J|LjMv8c(UiBs-$Lb^^Vonf&nYE8}s)T2yZcN|M+#Kh4NIzawl;`@B zx2vrRTRpP*oSlhrIUODzB?<$0YzM-=vmk$CL*b;n*&&XVY}w{OX5ZDG;D_GY&Lry* z`_bgddxSXoOd^9HbN6;aC?U7hA>*-`NR?a#fCG$1MFJBfnp(x%!dZZsGv#16A737+ zH8)$i77;No`Ayw|i0q@7rjD^Ji*Eb4#AF@mojHjfBKlu2`)El)K*-bp0000 Date: Sun, 8 Dec 2024 22:03:45 +0900 Subject: [PATCH 02/11] Finalize first draft of process_tusimple.py --- .../create_path/TuSimple/process_tusimple.py | 212 +++++++++++++----- PathDet/create_path/dataset_playground.ipynb | 86 ------- PathDet/create_path/test_output.png | Bin 795980 -> 0 bytes 3 files changed, 155 insertions(+), 143 deletions(-) delete mode 100644 PathDet/create_path/dataset_playground.ipynb delete mode 100644 PathDet/create_path/test_output.png diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index 8802899..8267d27 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -1,12 +1,9 @@ #! /usr/bin/env python3 import argparse -import os -import pathlib import json -import numpy as np +import os from PIL import Image, ImageDraw -import sys import warnings # ============================== Dataset structure ============================== # @@ -27,6 +24,25 @@ def normalizeCoords(lane, width, height): return [(x / width, y / height) for x, y in lane] + +def getLaneAnchor(lane): + + (x2, y2) = lane[-1] + (x1, y1) = lane[-2] + for i in range(len(lane) - 2, 0, -1): + if (lane[i][0] != x2): + (x1, y1) = lane[i] + break + if (x1 == x2): + warnings.warn(f"Vertical lane detected: {lane}, with these 2 anchors: ({x1}, {y1}), ({x2}, {y2}).") + return (x1, None, None) + a = (y2 - y1) / (x2 - x1) + b = y1 - a * x1 + x0 = (img_height - b) / a + + return (x0, b) + + def getEgoIndexes(anchors): # `anchors` is a list of tuples at form (xi, yi) @@ -35,29 +51,73 @@ def getEgoIndexes(anchors): for i in range(len(anchors)): if (anchors[i][0] >= img_width / 2): if (i == 0): - return "NO LANES on the LEFT side of frame. Something's wrong!" + return "NO LANES on the LEFT side of frame. Something's sussy out there!" left_ego_idx, right_ego_idx = i - 1, i return (left_ego_idx, right_ego_idx) - return "NO LANES on the RIGHT side of frame. Something's wrong!" - -def getLaneAnchor(lane): - - (x1, y1) = lane[len(lane) // 2] - (x2, y2) = lane[-1] - if (x1 == x2): - warnings.warn(f"Vertical lane detected: {lane}") - return (x1, None, None) - # print("x1, y1:", x1, y1) - # print("x2, y2:", x2, y2) - a = (y2 - y1) / (x2 - x1) - # print("a:", a) - b = y1 - a * x1 - # print("b:", b) - x0 = (img_height - b) / a - # print("x0:", x0) + return "NO LANES on the RIGHT side of frame. Something's sussy out there!" + + +def getDrivablePath(left_ego, right_ego, verbose = False): + i, j = 0, 0 + drivable_path = [] + while (i < len(left_ego) - 1 and j < len(right_ego) - 1): + if (left_ego[i][1] == right_ego[j][1]): + if (verbose): + print(f"Appending ({left_ego[i][0] + right_ego[j][0] / 2}, {left_ego[i][1]})") + drivable_path.append(( + (left_ego[i][0] + right_ego[j][0]) / 2, # Midpoint along x axis + left_ego[i][1] + )) + i += 1 + j += 1 + elif (left_ego[i][1] < right_ego[j][1]): + i += 1 + else: + j += 1 + + return drivable_path + + +def annotateGT( + anno_entry, anno_raw_file, + raw_dir, labeled_dir, + normalized = True +): + + # Load raw image + raw_img = Image.open(anno_raw_file).convert("RGB") + + # Copy raw img and put it in raw dir. + # Keep original pathname for traceability, but replace "/" with "-" + raw_img.save(os.path.join(raw_dir, str(anno_raw_file).replace("/", "-"))) - return (x0, b) + # Draw all lanes & lines + draw = ImageDraw.Draw(raw_img) + lane_colors = { + "outer_red": (255, 0, 0), + "ego_green": (0, 255, 0), + "drive_path_yellow": (255, 255, 0) + } + lane_w = 5 + # Draw lanes + for idx, lane in enumerate(anno_entry["lanes"]): + if (normalized): + lane = [(x * img_width, y * img_height) for x, y in lane] + if (idx in anno_entry["ego_indexes"]): + # Ego lanes, in green + draw.line(lane, fill = lane_colors["ego_green"], width = lane_w) + else: + # Outer lanes, in red + draw.line(lane, fill = lane_colors["outer_red"], width = lane_w) + # Drivable path, in yellow + if (normalized): + anno_entry["drivable_path"] = [(x * img_width, y * img_height) for x, y in anno_entry["drivable_path"]] + draw.line(anno_entry["drivable_path"], fill = lane_colors["drive_path_yellow"], width = lane_w) + + # Save labeled img, same format with raw, just different dir + raw_img.save(os.path.join(labeled_dir, str(anno_raw_file).replace('/', '-'))) + def parseAnnotations(anno_path): @@ -68,37 +128,46 @@ def parseAnnotations(anno_path): # Parse em anno_data = {} for item in read_data: + # Read raw data lanes = item["lanes"] h_samples = item["h_samples"] raw_file = item["raw_file"] + # Decouple from {lanes: [xi1, xi2, ...], h_samples: [y1, y2, ...]} to [(xi1, y1), (xi2, y2), ...] lanes_decoupled = [ [(x, y) for x, y in zip(lane, h_samples) if x != -2] - for lane in lanes + for lane in lanes if sum(1 for x in lane if x != -2) >= 2 # Filter out lanes < 2 points (there's actually a bunch of em) ] + # Determine 2 ego lanes lane_anchors = [getLaneAnchor(lane) for lane in lanes_decoupled] - ego_indexes = getEgoIndexes(lane_anchors) if (type(ego_indexes) is str): if (ego_indexes.startswith("NO")): - warnings.warn(f"Error parsing {raw_file}: {ego_indexes}") + warnings.warn(f"Parsing {raw_file}: {ego_indexes}") continue left_ego = lanes_decoupled[ego_indexes[0]] right_ego = lanes_decoupled[ego_indexes[1]] + # Determine drivable path from 2 egos + if raw_file == "clips/0313-1/960/20.jpg": + print(f"Processing {raw_file}...") + drivable_path = getDrivablePath(left_ego, right_ego, verbose = True) + else: + drivable_path = getDrivablePath(left_ego, right_ego) + # Parse processed data, all coords normalized anno_data[raw_file] = { "lanes": [normalizeCoords(lane, img_width, img_height) for lane in lanes_decoupled], "ego_indexes": ego_indexes, + "drivable_path": normalizeCoords(drivable_path, img_width, img_height), "img_size": (img_width, img_height), } - # - return anno_data + if __name__ == "__main__": @@ -117,41 +186,70 @@ def parseAnnotations(anno_path): type = str, help = "Output directory" ) - parser.add_argument( - "--test_label_file", - type = str - ) args = parser.parse_args() - dataset_dir = pathlib.Path(args.dataset_dir) - output_dir = pathlib.Path(args.output_dir) - output_dir.mkdir(exist_ok = True, parents = True) - test_label_file = args.test_label_file + dataset_dir = args.dataset_dir + + # Generate output structure + # --train + # | + # |----raw + # |----labeled + # --test + # | + # |----raw + # |----labeled + output_dir = args.output_dir + list_batches = ["train", "test"] + list_subbatches = ["raw", "labeled"] + for batch in list_batches: + for subbatch in list_subbatches: + sub_dir = os.path.join(output_dir, batch, subbatch) + if (not os.path.exists(sub_dir)): + os.makedirs(sub_dir, exist_ok = True) # ============================== Parsing annotations ============================== # train_label_files = [ - dataset_dir / root_dir / train_dir / f"label_data_{clip_code}.json" + os.path.join(dataset_dir, root_dir, train_dir, f"label_data_{clip_code}.json") for clip_code in train_clip_codes ] - # Make no mistake: the file `TuSimple/test_set/test_tasks_0627.json`` is NOT a label file. - # It is just a template for test submission. Kinda weird they put it in `test_set` while - # the actual label file is in `TuSimple/test_label.json`. - test_label_files = [dataset_dir / root_dir / test_file] - - # Getting list of all json files - # json_files = sorted([f for f in dataset_dir.glob("*.json")]) - - # for json_file in json_files: - # parseAnnotations(json_file) - - # print(train_label_files) - # print(test_label_files) - - first_file = "test_set/clips/0530/1492626760788443246_0/20.jpg" - - test_data = parseAnnotations(test_label_file) - first_test_data = test_data[first_file] - - print(first_test_data) \ No newline at end of file + """ + Make no mistake: the file `TuSimple/test_set/test_tasks_0627.json` is NOT a label file. + It is just a template for test submission. Kinda weird they put it in `test_set` while + the actual label file is in `TuSimple/test_label.json`. + """ + test_label_files = [os.path.join(dataset_dir, root_dir, test_file)] + + # Parse data by batch + data_master = { + "train": { + "files": train_label_files, + "data": {} + }, + "test": { + "files": test_label_files, + "data": {} + }, + } + for batch in list_batches: + print(f"\n================================== Processing {batch} data ==================================\n") + for anno_file in data_master[batch]["files"]: + print(f"Processing {batch} file {anno_file}...") + this_data = parseAnnotations(anno_file) + for raw_file, anno_entry in this_data.items(): + # Annotate raw images + annotateGT( + anno_entry, + os.path.join(dataset_dir, root_dir, f"{batch}_set", raw_file), + os.path.join(output_dir, batch, "raw"), + os.path.join(output_dir, batch, "labeled") + ) + data_master[batch]["data"].update(this_data) + print(f"Processed {len(this_data)} {batch} entries in file {anno_file}.") + print(f"Done processing {batch} data with {len(data_master[batch]['data'])} entries in total.\n") + + # Save master data + with open(os.path.join(output_dir, "data_master.json"), "w") as f: + json.dump(data_master, f, indent = 4) \ No newline at end of file diff --git a/PathDet/create_path/dataset_playground.ipynb b/PathDet/create_path/dataset_playground.ipynb deleted file mode 100644 index a593dcd..0000000 --- a/PathDet/create_path/dataset_playground.ipynb +++ /dev/null @@ -1,86 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import pathlib\n", - "import json\n", - "import numpy as np\n", - "from PIL import Image, ImageDraw\n", - "import sys" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Saved output image with lanes overlayed: ./test_output.png\n" - ] - } - ], - "source": [ - "def draw_lanes_on_image(label_file, image_file, output_file):\n", - " # Load the label file\n", - " with open(label_file, 'r') as f:\n", - " label_data = json.load(f)\n", - "\n", - " lanes = label_data['lanes']\n", - " h_samples = label_data['h_samples']\n", - " raw_file = label_data['raw_file']\n", - "\n", - " # Load the original image\n", - " image = Image.open(image_file).convert(\"RGB\")\n", - " draw = ImageDraw.Draw(image)\n", - "\n", - " # Define colors for lanes\n", - " lane_colors = [(255, 0, 0), (0, 255, 0), (0, 0, 255), (255, 255, 0)] # Red, Green, Blue, Yellow\n", - "\n", - " # Draw each lane\n", - " for idx, lane in enumerate(lanes):\n", - " points = [(x, y) for x, y in zip(lane, h_samples) if x != -2]\n", - " if points:\n", - " draw.line(points, fill=lane_colors[idx % len(lane_colors)], width=5)\n", - "\n", - " # Save the output image\n", - " image.save(output_file, \"PNG\")\n", - " print(f\"Saved output image with lanes overlayed: {output_file}\")\n", - "\n", - "# Example usage\n", - "label_file = \"/home/tranhuunhathuy/Downloads/tusiumple_1linesample.json\" # Path to the label JSON file\n", - "image_file = \"/home/tranhuunhathuy/Documents/Autoware/pov_datasets/tu_simple/TUSimple/test_set/clips/0530/1492626760788443246_0/20.jpg\" # Path to the input image\n", - "output_file = \"./test_output.png\" # Path to save the output image\n", - "\n", - "draw_lanes_on_image(label_file, image_file, output_file)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "usr", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/PathDet/create_path/test_output.png b/PathDet/create_path/test_output.png deleted file mode 100644 index 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zeJ-V4z9RWx?kLUm+(<5!bBfn-RVY!Qw=y6XmI75Z8m3Iw*I1{!tB8Qq)rh9Az40g4 z<$7hI2RS#s2xNP97P-CLUbiov`XsP4I1?``x2tb%c*4k;31Wab!A;eenyPMH0R>se zN;i^r+x!)zM&@M3PEDjO?_a-c%gb{8>BS{8c|Pw?W@4u9 z7DMLid3hRF7(h_%YGH^DbDHLma{$@Y2gM|-2az~wvwvg7khIlXi>Vm9#*pG5OMNP~ zIDtSoq6CWL2{YvlYy;lj68$59&W+vL%&QgY~a0&b5 z!tyz_iQNfM^?ojvJip0LGc)(8+05kO{A+?Yd0nNT%9<+2PUNr5Cs|yE1X1`c9TkZr z)jW5f5p|lip_Xg)Cx^**y@UkpI9L~_*JB?0wEioZ}p0wufbZAhBJ#HF|Xk@!`5 z6(v(SY{%6T(wf=2USjVt$0fbSC_eZ65XeyBkf4~fu{%a5039=YSM}i-yO?%}%n>z& zh0By3Kl*og2J|LjMv8c(UiBs-$Lb^^Vonf&nYE8}s)T2yZcN|M+#Kh4NIzawl;`@B zx2vrRTRpP*oSlhrIUODzB?<$0YzM-=vmk$CL*b;n*&&XVY}w{OX5ZDG;D_GY&Lry* z`_bgddxSXoOd^9HbN6;aC?U7hA>*-`NR?a#fCG$1MFJBfnp(x%!dZZsGv#16A737+ zH8)$i77;No`Ayw|i0q@7rjD^Ji*Eb4#AF@mojHjfBKlu2`)El)K*-bp0000 Date: Mon, 9 Dec 2024 00:15:11 +0900 Subject: [PATCH 03/11] working on docs, 20241209 00:15 --- .../assets/data_preprocessing_flow.drawio | 0 .../TuSimple/assets/tusimple_data_structure | 32 +++++++++++++++++++ 2 files changed, 32 insertions(+) create mode 100644 PathDet/create_path/TuSimple/assets/data_preprocessing_flow.drawio create mode 100644 PathDet/create_path/TuSimple/assets/tusimple_data_structure diff --git a/PathDet/create_path/TuSimple/assets/data_preprocessing_flow.drawio b/PathDet/create_path/TuSimple/assets/data_preprocessing_flow.drawio new file mode 100644 index 0000000..e69de29 diff --git a/PathDet/create_path/TuSimple/assets/tusimple_data_structure b/PathDet/create_path/TuSimple/assets/tusimple_data_structure new file mode 100644 index 0000000..cc2914f --- /dev/null +++ b/PathDet/create_path/TuSimple/assets/tusimple_data_structure @@ -0,0 +1,32 @@ +tu_simple (freshly extracted from .zip) +├── test_label_new.json +└── TUSimple + ├── test_label.json + ├── test_set + │ ├── clips + │ │ ├── 0530 [1248 folders x 20 images] + │ │ ├── 0531 [715 folders x 20 images] + │ │ └── 0601 [819 folders x 20 images] + │ ├── readme.md + │ └── test_tasks_0627.json + └── train_set + ├── clips + │ ├── 0313-1 [1355 folders x 20 images] + │ ├── 0313-2 [1503 folders x 20 images] + │ ├── 0531 [358 folders x 20 images] + │ └── 0601 [410 folders x 20 images] + ├── label_data_0313.json + ├── label_data_0531.json + ├── label_data_0601.json + ├── readme.md + └── seg_label + ├── 0313-1 [1355 folders x 20 images] + ├── 0313-2 [1503 folders x 20 images] + ├── 0530 [1248 folders x 20 images] + ├── 0531 [1073 folders x 20 images] + ├── 0601 [1229 folders x 20 images] + ├── list + │ ├── test_gt.txt + │ └── train_val_gt.txt + ├── test.json + └── train_val.json From b4e5064607d64b2217458005363c08985af4d590 Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Mon, 9 Dec 2024 00:53:01 +0900 Subject: [PATCH 04/11] config warning format --- .../create_path/TuSimple/process_tusimple.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index 8267d27..3a0b07e 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -6,6 +6,12 @@ from PIL import Image, ImageDraw import warnings +# Custom warning format cuz the default one is wayyyyyy too verbose +def custom_warning_format(message, category, filename, lineno, line=None): + return f"{message}\n" + +warnings.formatwarning = custom_warning_format + # ============================== Dataset structure ============================== # root_dir = "TUSimple" @@ -58,13 +64,11 @@ def getEgoIndexes(anchors): return "NO LANES on the RIGHT side of frame. Something's sussy out there!" -def getDrivablePath(left_ego, right_ego, verbose = False): +def getDrivablePath(left_ego, right_ego): i, j = 0, 0 drivable_path = [] while (i < len(left_ego) - 1 and j < len(right_ego) - 1): if (left_ego[i][1] == right_ego[j][1]): - if (verbose): - print(f"Appending ({left_ego[i][0] + right_ego[j][0] / 2}, {left_ego[i][1]})") drivable_path.append(( (left_ego[i][0] + right_ego[j][0]) / 2, # Midpoint along x axis left_ego[i][1] @@ -152,11 +156,7 @@ def parseAnnotations(anno_path): right_ego = lanes_decoupled[ego_indexes[1]] # Determine drivable path from 2 egos - if raw_file == "clips/0313-1/960/20.jpg": - print(f"Processing {raw_file}...") - drivable_path = getDrivablePath(left_ego, right_ego, verbose = True) - else: - drivable_path = getDrivablePath(left_ego, right_ego) + drivable_path = getDrivablePath(left_ego, right_ego) # Parse processed data, all coords normalized anno_data[raw_file] = { @@ -247,7 +247,7 @@ def parseAnnotations(anno_path): os.path.join(output_dir, batch, "labeled") ) data_master[batch]["data"].update(this_data) - print(f"Processed {len(this_data)} {batch} entries in file {anno_file}.") + print(f"Processed {len(this_data)} {batch} entries in above file.\n") print(f"Done processing {batch} data with {len(data_master[batch]['data'])} entries in total.\n") # Save master data From b2480cc6763dbcd7024adb3908c3a07597557ccc Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Mon, 9 Dec 2024 02:26:58 +0900 Subject: [PATCH 05/11] add a bunch of docstrings and some prettifying --- .../create_path/TuSimple/process_tusimple.py | 213 ++++++++++++++++-- 1 file changed, 193 insertions(+), 20 deletions(-) diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index 3a0b07e..659a981 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -3,12 +3,13 @@ import argparse import json import os +import pathlib from PIL import Image, ImageDraw import warnings # Custom warning format cuz the default one is wayyyyyy too verbose def custom_warning_format(message, category, filename, lineno, line=None): - return f"{message}\n" + return f"WARNING : {message}\n" warnings.formatwarning = custom_warning_format @@ -27,12 +28,54 @@ def custom_warning_format(message, category, filename, lineno, line=None): # ============================== Helper functions ============================== # def normalizeCoords(lane, width, height): + """ + Normalize the coords of lane points. + + Parameters + ---------- + lane (list of tuples): + - list of (x, y) tuples representing 2D coords of lane points. + - from here please be reminded that all these `lane` used in this script are in + ascending order of y-coords, which means it starts from top to bottom. + width (float): + - image width, 1280 for TuSimple. + height (float): + - image height, 720 for TuSimple. + + Returns + ------- + normalized lane: + - list of (x, y) tuples with normalized coords. + """ return [(x / width, y / height) for x, y in lane] def getLaneAnchor(lane): + """ + Determine "anchor" point of a lane. + + Here I define, the anchor of a lane is the intersection point of a lane with the bottom edge + of an image, determined by the lane's linear equation, defined by its 2 points: + - (x2, y2): last point of the lane, closest to bottom edge (where y = `img_height` = 720). + - (x1, y1): closest point to (x2, y2) but with different x-coord. + + With these 2 points, slope `a` and y-intercept `b` of the line equation `y = ax + b` can be + derived, as well as anchor point `x0`. + Parameters + ---------- + lane (list of tuples): + - list of (x, y) tuples representing 2D coords of lane points. + + Returns + ------- + tuple (x0, a, b): + - x0 (float): anchor point, representing (x0, y = 720). + - a (float): slope. + - b (float): y-intercept. + + """ (x2, y2) = lane[-1] (x1, y1) = lane[-2] for i in range(len(lane) - 2, 0, -1): @@ -46,14 +89,36 @@ def getLaneAnchor(lane): b = y1 - a * x1 x0 = (img_height - b) / a - return (x0, b) + return (x0, a, b) def getEgoIndexes(anchors): + """ + Identifies 2 ego lanes - left and right - from a sorted list of lane anchors. + + Basically, left and right ego lanes are the 2 lanes closest to the center of the frame. + Leveraging those "anchor" points, I pick the 2 anchors closest to center point of bottom + edge (640, 720), left and right. Their lanes are ego lanes. + + This is true like 99% of the time, and is a good heuristic for this dataset. Of course it + might mess up if the car is not driving straight, but these datasets are mostly from a + car cruising on highways, so it's fine ig. + + Parameters + ---------- + anchors (list of tuples): + - list of (x, y) tuples representing the anchors of lanes. + - In those labels, lanes are labeled from left to right, so anchors extracted from + them are also sorted x-coords in ascending order. + + Returns + ------- + tuple (left_ego_idx, right_ego_idx): + - 2 indexes in the original lane list, indicating left and right ego lanes. + + Sometimes there's no lanes on one side of the frame, so I return a string to indicate that. - # `anchors` is a list of tuples at form (xi, yi) - # In those labels, lanes are labeled from left to right, so these anchors - # extracted from them are also sorted x-coords in ascending order. + """ for i in range(len(anchors)): if (anchors[i][0] >= img_width / 2): if (i == 0): @@ -65,6 +130,24 @@ def getEgoIndexes(anchors): def getDrivablePath(left_ego, right_ego): + """ + Computes drivable path as midpoint between 2 ego lanes, basically the main point of this task. + + Average is taken with points having same y-coord. If not, skip to ensure alignment. + + Parameters + ---------- + left_ego (list of tuples): + - list of (x, y) points representing left ego lane. + right_ego (list of tuples): + - same as above, for right ego lane. + + Returns + ------- + drivable_path (list of tuples): + - list of (x, y) points representing drivable path. + + """ i, j = 0, 0 drivable_path = [] while (i < len(left_ego) - 1 and j < len(right_ego) - 1): @@ -88,13 +171,47 @@ def annotateGT( raw_dir, labeled_dir, normalized = True ): + """ + Annotates and saves an image with lane and drivable path overlays for GT generation. + There are both raw and labeled images saved on 2 separated dirs. + + Parameters + ---------- + anno_entry (dict): + - an annotation entry containing: + + `lanes` (list of list of tuples): a list of lane points, each represented as + (x, y) tuples. Coords may be normalized (0 to 1) or absolute. + + `ego_indexes` (list of int): indexes of ego lanes in the `lanes` list. + + `drivable_path` (list of tuples): drivable path as a list of (x, y) tuples. + anno_raw_file (str): + - file path of raw input image to annotate. + raw_dir (str): + - directory to save raw (unlabeled) image copy. + labeled_dir (str): + - directory to save annotated (labeled) image. + normalized (bool, optional): + - defaults to `True`. + - If `True`, all coords are scaled/normalized to (0, 1). Otherwise, absolute. + + In labeled image, lanes have different colors: + - Outer lanes: red. + - Ego lanes: green. + - Drivable path: yellow. + + No returns + ---------- + + """ # Load raw image raw_img = Image.open(anno_raw_file).convert("RGB") + # Define save name + # Keep original pathname (back to 4 levels) for traceability, but replace "/" with "-" + save_name = "_".join(anno_raw_file.split("/")[-4 : ]) + # Copy raw img and put it in raw dir. - # Keep original pathname for traceability, but replace "/" with "-" - raw_img.save(os.path.join(raw_dir, str(anno_raw_file).replace("/", "-"))) + raw_img.save(os.path.join(raw_dir, save_name)) # Draw all lanes & lines draw = ImageDraw.Draw(raw_img) @@ -120,19 +237,45 @@ def annotateGT( draw.line(anno_entry["drivable_path"], fill = lane_colors["drive_path_yellow"], width = lane_w) # Save labeled img, same format with raw, just different dir - raw_img.save(os.path.join(labeled_dir, str(anno_raw_file).replace('/', '-'))) + raw_img.save(os.path.join(labeled_dir, save_name)) def parseAnnotations(anno_path): + """ + Parses lane annotations from raw dataset file, then extracts normalized GT data. + + First, read raw annotation/label data, then filter and process lane info, then identify 2 + ego lanes, and calculate drivable path. All coords are normalized. Basically "main" function. + + Parameters + ---------- + anno_path (str): + - path to annotation file containing lane data in JSON lines format. + + Returns + ------- + anno_data (dict): + - dictionary mapping `raw_file` paths to their corresponding processed annotations. + - each entry contains: + + `lanes` (list of list of tuples): normalized lane points for each lane. + + `ego_indexes` (tuple): indexes of 2 left and right ego lanes. + + `drivable_path` (list of tuples): normalized points of the drivable path. + + `img_size` (tuple): dimensions of images (width, height). TuSimple is 1280 x 720. + + Notes + ----- + - Lanes with fewer than 2 valid points (x != 2) are ignored. + - All coords are normalized, as requested by Mr. Zain. + - Warnings are issued for frames with no lanes on one side, while finding ego indexes. + """ # Read em raw with open(anno_path, "r") as f: read_data = [json.loads(line) for line in f.readlines()] - # Parse em + # Parse em through anno_data = {} for item in read_data: - # Read raw data lanes = item["lanes"] h_samples = item["h_samples"] raw_file = item["raw_file"] @@ -170,6 +313,37 @@ def parseAnnotations(anno_path): if __name__ == "__main__": + """ + TuSimple dataset preprocessing script for PathDet. + + CMD line args + ------------- + --dataset_dir : str + - path to TuSimple dataset directory. + - only accepts the dir right after extraction. So it should be `/tu_simple` + if you tried to download it from Kaggle. + --output_dir : str + - path to output directory where processed files will be stored. + + Notes + ----- + - These dirs can either be relative or absolute. + + + Structure of `output dir`: + ------------------------ + --train + |----raw + |----labeled + --test + |----raw + |----labeled + + Example: + -------- + python process_tusimple.py --dataset_dir /path/to/TuSimple --output_dir /path/to/output + + """ # ============================== Parsing args ============================== # @@ -188,17 +362,16 @@ def parseAnnotations(anno_path): ) args = parser.parse_args() - dataset_dir = args.dataset_dir - # Generate output structure - # --train - # | - # |----raw - # |----labeled - # --test - # | - # |----raw - # |----labeled + """ + --train + |----raw + |----labeled + --test + |----raw + |----labeled + """ + dataset_dir = args.dataset_dir output_dir = args.output_dir list_batches = ["train", "test"] list_subbatches = ["raw", "labeled"] From 40872383799f250c96943a1aa4645423d2bd7a1e Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Mon, 9 Dec 2024 15:37:47 +0900 Subject: [PATCH 06/11] fix the non-normed drivable path bug --- PathDet/create_path/TuSimple/process_tusimple.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index 659a981..aa37ba5 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -233,8 +233,10 @@ def annotateGT( draw.line(lane, fill = lane_colors["outer_red"], width = lane_w) # Drivable path, in yellow if (normalized): - anno_entry["drivable_path"] = [(x * img_width, y * img_height) for x, y in anno_entry["drivable_path"]] - draw.line(anno_entry["drivable_path"], fill = lane_colors["drive_path_yellow"], width = lane_w) + drivable_renormed = [(x * img_width, y * img_height) for x, y in anno_entry["drivable_path"]] + else: + drivable_renormed = anno_entry["drivable_path"] + draw.line(drivable_renormed, fill = lane_colors["drive_path_yellow"], width = lane_w) # Save labeled img, same format with raw, just different dir raw_img.save(os.path.join(labeled_dir, save_name)) @@ -308,6 +310,7 @@ def parseAnnotations(anno_path): "drivable_path": normalizeCoords(drivable_path, img_width, img_height), "img_size": (img_width, img_height), } + print(anno_data[raw_file]["drivable_path"]) return anno_data @@ -341,7 +344,7 @@ def parseAnnotations(anno_path): Example: -------- - python process_tusimple.py --dataset_dir /path/to/TuSimple --output_dir /path/to/output + `python process_tusimple.py --dataset_dir /path/to/TuSimple --output_dir /path/to/output` """ From 4f88930b03a97f60360d165756bed15f50197269 Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Mon, 9 Dec 2024 23:07:14 +0900 Subject: [PATCH 07/11] post-review with Mr. Zain, some comments added --- PathDet/create_path/TuSimple/process_tusimple.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index aa37ba5..43cf2a1 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -271,11 +271,11 @@ def parseAnnotations(anno_path): - Warnings are issued for frames with no lanes on one side, while finding ego indexes. """ - # Read em raw + # Read each line of GT text file as JSON with open(anno_path, "r") as f: read_data = [json.loads(line) for line in f.readlines()] - # Parse em through + # Parse data from those JSON lines anno_data = {} for item in read_data: lanes = item["lanes"] @@ -283,6 +283,7 @@ def parseAnnotations(anno_path): raw_file = item["raw_file"] # Decouple from {lanes: [xi1, xi2, ...], h_samples: [y1, y2, ...]} to [(xi1, y1), (xi2, y2), ...] + # `lane_decoupled` is a list of sublists representing lanes, each lane is a list of (x, y) tuples. lanes_decoupled = [ [(x, y) for x, y in zip(lane, h_samples) if x != -2] for lane in lanes if sum(1 for x in lane if x != -2) >= 2 # Filter out lanes < 2 points (there's actually a bunch of em) From aeb3daf10730c3f0b78c0b736d5cd9aa96e66967 Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Tue, 10 Dec 2024 23:05:50 +0900 Subject: [PATCH 08/11] data structure & file org fixed --- PathDet/create_path/TuSimple/README.md | 1 - .../create_path/TuSimple/process_tusimple.py | 138 ++++++++++-------- 2 files changed, 76 insertions(+), 63 deletions(-) diff --git a/PathDet/create_path/TuSimple/README.md b/PathDet/create_path/TuSimple/README.md index 1403bef..c2552a6 100644 --- a/PathDet/create_path/TuSimple/README.md +++ b/PathDet/create_path/TuSimple/README.md @@ -1,3 +1,2 @@ ## process_tusimple.py -Placeholder diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index 43cf2a1..b4e1d2e 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -6,6 +6,7 @@ import pathlib from PIL import Image, ImageDraw import warnings +from datetime import datetime # Custom warning format cuz the default one is wayyyyyy too verbose def custom_warning_format(message, category, filename, lineno, line=None): @@ -168,12 +169,15 @@ def getDrivablePath(left_ego, right_ego): def annotateGT( anno_entry, anno_raw_file, - raw_dir, labeled_dir, + raw_dir, visualization_dir, mask_dir, + img_width, img_height, normalized = True ): """ - Annotates and saves an image with lane and drivable path overlays for GT generation. - There are both raw and labeled images saved on 2 separated dirs. + Annotates and saves an image with: + - Raw image, in "output_dir/image". + - Annotated image with all lanes, in "output_dir/visualization". + - Binary segmentation mask of drivable path, in "output_dir/segmentation". Parameters ---------- @@ -187,28 +191,32 @@ def annotateGT( - file path of raw input image to annotate. raw_dir (str): - directory to save raw (unlabeled) image copy. - labeled_dir (str): + visualization_dir (str): - directory to save annotated (labeled) image. + mask_dir (str): + - directory to save binary segmentation mask. normalized (bool, optional): - defaults to `True`. - If `True`, all coords are scaled/normalized to (0, 1). Otherwise, absolute. - In labeled image, lanes have different colors: + No returns + ---------- + + Notes + ----- + In visualization image, lanes have different colors: - Outer lanes: red. - Ego lanes: green. - Drivable path: yellow. - No returns - ---------- - """ # Load raw image raw_img = Image.open(anno_raw_file).convert("RGB") # Define save name - # Keep original pathname (back to 4 levels) for traceability, but replace "/" with "-" - save_name = "_".join(anno_raw_file.split("/")[-4 : ]) + # Keep original pathname (back to 5 levels) for traceability, but replace "/" with "-" + save_name = "_".join(anno_raw_file.split("/")[-5 : ]) # Copy raw img and put it in raw dir. raw_img.save(os.path.join(raw_dir, save_name)) @@ -238,8 +246,13 @@ def annotateGT( drivable_renormed = anno_entry["drivable_path"] draw.line(drivable_renormed, fill = lane_colors["drive_path_yellow"], width = lane_w) - # Save labeled img, same format with raw, just different dir - raw_img.save(os.path.join(labeled_dir, save_name)) + # Save visualization img, same format with raw, just different dir + raw_img.save(os.path.join(visualization_dir, save_name)) + + # Working on binary mask. Here I reuse raw_img for robustness. + draw.rectangle([0, 0, img_width, img_height], fill = 0) + draw.line(drivable_renormed, fill = 255, width = lane_w) + raw_img.save(os.path.join(mask_dir, save_name)) def parseAnnotations(anno_path): @@ -262,7 +275,8 @@ def parseAnnotations(anno_path): + `lanes` (list of list of tuples): normalized lane points for each lane. + `ego_indexes` (tuple): indexes of 2 left and right ego lanes. + `drivable_path` (list of tuples): normalized points of the drivable path. - + `img_size` (tuple): dimensions of images (width, height). TuSimple is 1280 x 720. + + `img_width` (float): image width. TuSimple is 1280. + + `img_height` (float): image height. TuSimple is 720. Notes ----- @@ -306,12 +320,12 @@ def parseAnnotations(anno_path): # Parse processed data, all coords normalized anno_data[raw_file] = { - "lanes": [normalizeCoords(lane, img_width, img_height) for lane in lanes_decoupled], - "ego_indexes": ego_indexes, - "drivable_path": normalizeCoords(drivable_path, img_width, img_height), - "img_size": (img_width, img_height), + "lanes" : [normalizeCoords(lane, img_width, img_height) for lane in lanes_decoupled], + "ego_indexes" : ego_indexes, + "drivable_path" : normalizeCoords(drivable_path, img_width, img_height), + "img_width" : img_width, + "img_height" : img_height, } - print(anno_data[raw_file]["drivable_path"]) return anno_data @@ -336,12 +350,11 @@ def parseAnnotations(anno_path): Structure of `output dir`: ------------------------ - --train - |----raw - |----labeled - --test - |----raw - |----labeled + --output_dir + |----image + |----segmentation + |----visualization + |----drivable_path.json Example: -------- @@ -368,22 +381,19 @@ def parseAnnotations(anno_path): # Generate output structure """ - --train - |----raw - |----labeled - --test - |----raw - |----labeled + --output_dir + |----image + |----segmentation + |----visualization + |----drivable_path.json """ dataset_dir = args.dataset_dir output_dir = args.output_dir - list_batches = ["train", "test"] - list_subbatches = ["raw", "labeled"] - for batch in list_batches: - for subbatch in list_subbatches: - sub_dir = os.path.join(output_dir, batch, subbatch) - if (not os.path.exists(sub_dir)): - os.makedirs(sub_dir, exist_ok = True) + list_subdirs = ["image", "segmentation", "visualization"] + for subdir in list_subdirs: + subdir_path = os.path.join(output_dir, subdir) + if (not os.path.exists(subdir_path)): + os.makedirs(subdir_path, exist_ok = True) # ============================== Parsing annotations ============================== # @@ -398,35 +408,39 @@ def parseAnnotations(anno_path): the actual label file is in `TuSimple/test_label.json`. """ test_label_files = [os.path.join(dataset_dir, root_dir, test_file)] + label_files = train_label_files + test_label_files # Parse data by batch data_master = { - "train": { - "files": train_label_files, - "data": {} - }, - "test": { - "files": test_label_files, - "data": {} - }, + "files": label_files, + "data": {} } - for batch in list_batches: - print(f"\n================================== Processing {batch} data ==================================\n") - for anno_file in data_master[batch]["files"]: - print(f"Processing {batch} file {anno_file}...") - this_data = parseAnnotations(anno_file) - for raw_file, anno_entry in this_data.items(): - # Annotate raw images - annotateGT( - anno_entry, - os.path.join(dataset_dir, root_dir, f"{batch}_set", raw_file), - os.path.join(output_dir, batch, "raw"), - os.path.join(output_dir, batch, "labeled") - ) - data_master[batch]["data"].update(this_data) - print(f"Processed {len(this_data)} {batch} entries in above file.\n") - print(f"Done processing {batch} data with {len(data_master[batch]['data'])} entries in total.\n") + + for anno_file in data_master["files"]: + print(f"\n==================== Processing data in label file {anno_file} ====================\n") + this_data = parseAnnotations(anno_file) + for raw_file, anno_entry in this_data.items(): + set_dir = "/".join(anno_file.split("/")[ : -1]) # Slap "train_set" or "test_set" to the end + set_dir = os.path.join(set_dir, test_dir) if test_file in anno_file else set_dir # Tricky test dir + # Annotate raw images + annotateGT( + anno_entry, + anno_raw_file = os.path.join(set_dir, raw_file), + raw_dir = os.path.join(output_dir, "image"), + visualization_dir = os.path.join(output_dir, "visualization"), + mask_dir = os.path.join(output_dir, "segmentation"), + img_height = anno_entry["img_height"], + img_width = anno_entry["img_width"] + ) + # Pop redundant keys + del anno_entry["lanes"] + del anno_entry["ego_indexes"] + + data_master["data"].update(this_data) + print(f"Processed {len(this_data)} entries in above file.\n") + + print(f"Done processing data with {len(data_master['data'])} entries in total.\n") # Save master data - with open(os.path.join(output_dir, "data_master.json"), "w") as f: + with open(os.path.join(output_dir, "drivable_path.json"), "w") as f: json.dump(data_master, f, indent = 4) \ No newline at end of file From bae6eb8c6f314e4e84e67db9b5753338c4ad9ede Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Tue, 10 Dec 2024 23:39:14 +0900 Subject: [PATCH 09/11] documentation, readme and no diagram --- PathDet/create_path/TuSimple/README.md | 180 +++++++++++++++++- .../assets/data_preprocessing_flow.drawio | 0 .../create_path/TuSimple/process_tusimple.py | 159 +--------------- 3 files changed, 180 insertions(+), 159 deletions(-) delete mode 100644 PathDet/create_path/TuSimple/assets/data_preprocessing_flow.drawio diff --git a/PathDet/create_path/TuSimple/README.md b/PathDet/create_path/TuSimple/README.md index c2552a6..b626a10 100644 --- a/PathDet/create_path/TuSimple/README.md +++ b/PathDet/create_path/TuSimple/README.md @@ -1,2 +1,180 @@ -## process_tusimple.py +# process_tusimple.py +## TuSimple dataset preprocessing script for PathDet. + +This script parse the [TuSimple lane detection dataset](https://www.kaggle.com/datasets/manideep1108/tusimple?resource=download) to create a dataset comprising input images in PNG format and a single drivable path as the ground truth, derived as the mid-line between the left/right ego lanes. + +## I. Functions + +### 1. `normalizeCoords()` + +Normalize the coords of lane points. + +#### a. Parameters +- `lane` (list of tuples): + - list of (x, y) tuples representing 2D coords of lane points. + - from here please be reminded that all these `lane` used in this script are in ascending order of y-coords, which means it starts from top to bottom. +- `width` (float): + - image width, 1280 for TuSimple. +- `height` (float): + - image height, 720 for TuSimple. + +#### b. Returns +- normalized lane: + - list of (x, y) tuples with normalized coords. + +### 2. `getLaneAnchor()` + +Determine "anchor" point of a lane. + +Here I define, the *anchor* of a lane is the intersection point of a lane with the bottom edge of an image, determined by the lane's linear equation, defined by its 2 points: +- `(x2, y2)`: last point of the lane, closest to bottom edge (where `y = img_height = 720`). +- `(x1, y1)`: closest point to `(x2, y2)` but with different x-coord. + +With these 2 points, slope `a` and y-intercept `b` of the line equation `y = ax + b` can be derived, as well as anchor point `x0`. + +#### a. Parameters +- `lane` (list of tuples): + - list of `(x, y)` tuples representing 2D coords of lane points. + +#### b. Returns +- tuple `(x0, a, b)`: + - `x0` (float): anchor point, representing `(x0, y = 720)`. + - `a` (float): slope. + - `b` (float): y-intercept. + +### 3. `getEgoIndexes()` + +Identifies 2 ego lanes - left and right - from a sorted list of lane anchors. + +Basically, left and right ego lanes are the 2 lanes closest to the center of the frame. +Leveraging those "anchor" points, I pick the 2 anchors closest to center point of bottom edge `(640, 720)`, left and right. Their lanes are ego lanes. + +This is true like 99% of the time, and is a good heuristic for this dataset. Of course it might mess up if the car is not driving straight, but these datasets are mostly from a car cruising on highways, so it's fine ig. + +#### a. Parameters + +- `anchors` (list of tuples): + - list of `(x, y)` tuples representing the anchors of lanes. + - In those labels, lanes are labeled from left to right, so anchors extracted from them are also sorted x-coords in ascending order. + +#### b. Returns +- `(left_ego_idx, right_ego_idx)` (tuple): + - 2 indexes in the original lane list, indicating left and right ego lanes. + +Sometimes there's no lanes on one side of the frame, so I return a string to indicate that. + +### 4. `getDrivablePath()` + +Computes drivable path as midpoint between 2 ego lanes, basically the main point of this task. + +Average is taken with points having same y-coord. If not, skip to ensure alignment. + +#### a. Parameters + +- `left_ego` (list of tuples): + - list of `(x, y)` points representing left ego lane. +- `right_ego` (list of tuples): + - same as above, for right ego lane. + +#### b. Returns +- `drivable_path` (list of tuples): + - list of `(x, y)` points representing drivable path. + +### 5. `annotateGT()` + +Annotates and saves an image with: +- Raw image, in `output_dir/image`. +- Annotated image with all lanes, in `output_dir/visualization`. +- Binary segmentation mask of drivable path, in `output_dir/segmentation`. + +#### a. Parameters + +- `anno_entry` (dict): + - an annotation entry containing: + + `lanes` (list of list of tuples): a list of lane points, each represented as `(x, y)` tuples. Coords may be normalized (0 to 1) or absolute. + + `ego_indexes` (list of int): indexes of ego lanes in the `lanes` list. + + `drivable_path` (list of tuples): drivable path as a list of `(x, y)` tuples. +- `anno_raw_file` (str): + - file path of raw input image to annotate. +- `raw_dir` (str): + - directory to save raw (unlabeled) image copy. +- `visualization_dir` (str): + - directory to save annotated (labeled) image. +- `mask_dir` (str): + - directory to save binary segmentation mask. +- `normalized` (bool, optional): + - defaults to `True`. + - if `True`, all coords are scaled/normalized to `(0, 1)`. Otherwise, absolute. + +#### b. Returns +No returns. + +#### c. Notes +In visualization image, different lanes have different colors: +- Outer lanes: red. +- Ego lanes: green. +- Drivable path: yellow. + +### 6. `parseAnnotations()` + +Parses lane annotations from raw dataset file, then extracts normalized GT data. + +First, read raw annotation/label data, then filter and process lane info, then identify 2 ego lanes, and calculate drivable path. All coords are normalized. Basically a "main" function. + +#### a. Parameters + +- `anno_path` (str): + - path to annotation file containing lane data in JSON lines format. + +#### b. Returns +- `anno_data` (dict): + - dictionary mapping `raw_file` paths to their corresponding processed annotations. + - each entry contains: + + `lanes` (list of list of tuples): normalized lane points for each lane. + + `ego_indexes` (tuple): indexes of 2 left and right ego lanes. + + `drivable_path` (list of tuples): normalized points of the drivable path. + + `img_width` (float): image width. TuSimple is 1280. + + `img_height` (float): image height. TuSimple is 720. + +#### c. Notes +- Lanes with fewer than 2 valid points `(x != 2)` are ignored. +- All coords are normalized, as requested by Mr. Zain. +- Warnings are issued for frames with no lanes on one side, while finding ego indexes. + +## II. Workflow & usage + +### 1. Workflow + +1. Read raw annotation/label data, get all lane info. +2. Determine ego lanes: + - Calculate lane “anchors”. + - Determine 2 anchors of 2 ego lanes. +3. Determine drivable path. +4. Parse everything to new index, all coords normalized. +5. Save a copy of raw img, and a labeled img with ego, drivable path, & others. + +### 2. Usage + +#### a. Cmd line args + +- `dataset_dir` : str + - path to TuSimple dataset directory. + - only accepts the dir right after extraction. So it should be `/tu_simple` if you tried to download it from Kaggle. +- `output_dir` : str + - path to output directory where processed files will be stored. These dirs can either be relative or absolute. + +#### b. Example + +``` +`python process_tusimple.py --dataset_dir /path/to/TuSimple --output_dir /path/to/output` +``` + +Structure of `output_dir`: +``` +--output_dir + |----image + |----segmentation + |----visualization + |----drivable_path.json +``` \ No newline at end of file diff --git a/PathDet/create_path/TuSimple/assets/data_preprocessing_flow.drawio b/PathDet/create_path/TuSimple/assets/data_preprocessing_flow.drawio deleted file mode 100644 index e69de29..0000000 diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index b4e1d2e..6178d0b 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -32,22 +32,6 @@ def normalizeCoords(lane, width, height): """ Normalize the coords of lane points. - Parameters - ---------- - lane (list of tuples): - - list of (x, y) tuples representing 2D coords of lane points. - - from here please be reminded that all these `lane` used in this script are in - ascending order of y-coords, which means it starts from top to bottom. - width (float): - - image width, 1280 for TuSimple. - height (float): - - image height, 720 for TuSimple. - - Returns - ------- - normalized lane: - - list of (x, y) tuples with normalized coords. - """ return [(x / width, y / height) for x, y in lane] @@ -56,26 +40,6 @@ def getLaneAnchor(lane): """ Determine "anchor" point of a lane. - Here I define, the anchor of a lane is the intersection point of a lane with the bottom edge - of an image, determined by the lane's linear equation, defined by its 2 points: - - (x2, y2): last point of the lane, closest to bottom edge (where y = `img_height` = 720). - - (x1, y1): closest point to (x2, y2) but with different x-coord. - - With these 2 points, slope `a` and y-intercept `b` of the line equation `y = ax + b` can be - derived, as well as anchor point `x0`. - - Parameters - ---------- - lane (list of tuples): - - list of (x, y) tuples representing 2D coords of lane points. - - Returns - ------- - tuple (x0, a, b): - - x0 (float): anchor point, representing (x0, y = 720). - - a (float): slope. - - b (float): y-intercept. - """ (x2, y2) = lane[-1] (x1, y1) = lane[-2] @@ -97,28 +61,6 @@ def getEgoIndexes(anchors): """ Identifies 2 ego lanes - left and right - from a sorted list of lane anchors. - Basically, left and right ego lanes are the 2 lanes closest to the center of the frame. - Leveraging those "anchor" points, I pick the 2 anchors closest to center point of bottom - edge (640, 720), left and right. Their lanes are ego lanes. - - This is true like 99% of the time, and is a good heuristic for this dataset. Of course it - might mess up if the car is not driving straight, but these datasets are mostly from a - car cruising on highways, so it's fine ig. - - Parameters - ---------- - anchors (list of tuples): - - list of (x, y) tuples representing the anchors of lanes. - - In those labels, lanes are labeled from left to right, so anchors extracted from - them are also sorted x-coords in ascending order. - - Returns - ------- - tuple (left_ego_idx, right_ego_idx): - - 2 indexes in the original lane list, indicating left and right ego lanes. - - Sometimes there's no lanes on one side of the frame, so I return a string to indicate that. - """ for i in range(len(anchors)): if (anchors[i][0] >= img_width / 2): @@ -134,20 +76,6 @@ def getDrivablePath(left_ego, right_ego): """ Computes drivable path as midpoint between 2 ego lanes, basically the main point of this task. - Average is taken with points having same y-coord. If not, skip to ensure alignment. - - Parameters - ---------- - left_ego (list of tuples): - - list of (x, y) points representing left ego lane. - right_ego (list of tuples): - - same as above, for right ego lane. - - Returns - ------- - drivable_path (list of tuples): - - list of (x, y) points representing drivable path. - """ i, j = 0, 0 drivable_path = [] @@ -179,36 +107,6 @@ def annotateGT( - Annotated image with all lanes, in "output_dir/visualization". - Binary segmentation mask of drivable path, in "output_dir/segmentation". - Parameters - ---------- - anno_entry (dict): - - an annotation entry containing: - + `lanes` (list of list of tuples): a list of lane points, each represented as - (x, y) tuples. Coords may be normalized (0 to 1) or absolute. - + `ego_indexes` (list of int): indexes of ego lanes in the `lanes` list. - + `drivable_path` (list of tuples): drivable path as a list of (x, y) tuples. - anno_raw_file (str): - - file path of raw input image to annotate. - raw_dir (str): - - directory to save raw (unlabeled) image copy. - visualization_dir (str): - - directory to save annotated (labeled) image. - mask_dir (str): - - directory to save binary segmentation mask. - normalized (bool, optional): - - defaults to `True`. - - If `True`, all coords are scaled/normalized to (0, 1). Otherwise, absolute. - - No returns - ---------- - - Notes - ----- - In visualization image, lanes have different colors: - - Outer lanes: red. - - Ego lanes: green. - - Drivable path: yellow. - """ # Load raw image @@ -258,32 +156,7 @@ def annotateGT( def parseAnnotations(anno_path): """ Parses lane annotations from raw dataset file, then extracts normalized GT data. - - First, read raw annotation/label data, then filter and process lane info, then identify 2 - ego lanes, and calculate drivable path. All coords are normalized. Basically "main" function. - - Parameters - ---------- - anno_path (str): - - path to annotation file containing lane data in JSON lines format. - - Returns - ------- - anno_data (dict): - - dictionary mapping `raw_file` paths to their corresponding processed annotations. - - each entry contains: - + `lanes` (list of list of tuples): normalized lane points for each lane. - + `ego_indexes` (tuple): indexes of 2 left and right ego lanes. - + `drivable_path` (list of tuples): normalized points of the drivable path. - + `img_width` (float): image width. TuSimple is 1280. - + `img_height` (float): image height. TuSimple is 720. - - Notes - ----- - - Lanes with fewer than 2 valid points (x != 2) are ignored. - - All coords are normalized, as requested by Mr. Zain. - - Warnings are issued for frames with no lanes on one side, while finding ego indexes. - + """ # Read each line of GT text file as JSON with open(anno_path, "r") as f: @@ -331,36 +204,6 @@ def parseAnnotations(anno_path): if __name__ == "__main__": - """ - TuSimple dataset preprocessing script for PathDet. - - CMD line args - ------------- - --dataset_dir : str - - path to TuSimple dataset directory. - - only accepts the dir right after extraction. So it should be `/tu_simple` - if you tried to download it from Kaggle. - --output_dir : str - - path to output directory where processed files will be stored. - - Notes - ----- - - These dirs can either be relative or absolute. - - - Structure of `output dir`: - ------------------------ - --output_dir - |----image - |----segmentation - |----visualization - |----drivable_path.json - - Example: - -------- - `python process_tusimple.py --dataset_dir /path/to/TuSimple --output_dir /path/to/output` - - """ # ============================== Parsing args ============================== # From bc1ac29d74e95ecb99735dcb78f0378bc04f1e57 Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Wed, 11 Dec 2024 00:56:14 +0900 Subject: [PATCH 10/11] drivable path extension --- .../create_path/TuSimple/process_tusimple.py | 28 +++++++++++++++++-- 1 file changed, 26 insertions(+), 2 deletions(-) diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index 6178d0b..da72087 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -92,6 +92,29 @@ def getDrivablePath(left_ego, right_ego): else: j += 1 + # Extend drivable path to bottom edge of the frame + if (len(drivable_path) >= 2): + x1, y1 = drivable_path[-2] + x2, y2 = drivable_path[-1] + if (x2 == x1): + x_bottom = x2 + else: + a = (y2 - y1) / (x2 - x1) + x_bottom = x2 + (img_height - y2) / a + drivable_path.append((x_bottom, img_height)) + + # Extend drivable path to be on par with longest ego lane + y_top = min(left_ego[0][1], right_ego[0][1]) + if len(drivable_path) >= 2: + x1, y1 = drivable_path[0] + x2, y2 = drivable_path[1] + if (x2 == x1): + x_top = x1 + else: + a = (y2 - y1) / (x2 - x1) + x_top = x1 + (y_top - y1) / a + drivable_path.insert(0, (x_top, y_top)) + return drivable_path @@ -114,7 +137,8 @@ def annotateGT( # Define save name # Keep original pathname (back to 5 levels) for traceability, but replace "/" with "-" - save_name = "_".join(anno_raw_file.split("/")[-5 : ]) + # Also save in PNG (EXTREMELY SLOW compared to jpg, for lossless quality) + save_name = "_".join(anno_raw_file.split("/")[-5 : ]).replace(".jpg", ".png") # Copy raw img and put it in raw dir. raw_img.save(os.path.join(raw_dir, save_name)) @@ -156,7 +180,7 @@ def annotateGT( def parseAnnotations(anno_path): """ Parses lane annotations from raw dataset file, then extracts normalized GT data. - + """ # Read each line of GT text file as JSON with open(anno_path, "r") as f: From 23cc049c9404a3cf9be8581ed3ca7e62c0912b95 Mon Sep 17 00:00:00 2001 From: TranHuuNhatHuy Date: Fri, 13 Dec 2024 02:44:43 +0900 Subject: [PATCH 11/11] final touches - minor fixes --- PathDet/create_path/TuSimple/README.md | 22 ++++- .../create_path/TuSimple/process_tusimple.py | 92 +++++++++++++------ 2 files changed, 83 insertions(+), 31 deletions(-) diff --git a/PathDet/create_path/TuSimple/README.md b/PathDet/create_path/TuSimple/README.md index b626a10..1b9a6b8 100644 --- a/PathDet/create_path/TuSimple/README.md +++ b/PathDet/create_path/TuSimple/README.md @@ -2,7 +2,27 @@ ## TuSimple dataset preprocessing script for PathDet. -This script parse the [TuSimple lane detection dataset](https://www.kaggle.com/datasets/manideep1108/tusimple?resource=download) to create a dataset comprising input images in PNG format and a single drivable path as the ground truth, derived as the mid-line between the left/right ego lanes. +This script parse the [TuSimple lane detection dataset](https://www.kaggle.com/datasets/manideep1108/tusimple?resource=download) (24GB) to create a dataset comprising input images in PNG format and a single drivable path as the ground truth, derived as the mid-line between the left/right ego lanes. + +- Data acquisition: by TuSimple - an autonomous trucking company, with 6,408 road images on US highways. +- Features: + - Different conditions (weather, light, highway, traffic, etc.) + - Lane detection competition in CVPR 2017 WAD. +- Annotation method: polylines for lane markings + +- TuSimple directory structure (from download) + - `clips/` : video clips + - `some_clip/` : sequential images, 20 frames + - `tasks.json` : label data in training set, and a submission template for testing set. + +- Data label format: + - `raw_file` : (str) 20th frame file path in a clip + - `lanes` : (list) lists of lanes, each list represents a lane. Each element is X-coordinate across the polyline. + - `h_samples` : (list) list of Y-coordinate across the polyline. + - `-2` means there is no existing lane marking. + - Normally each frame has 4 lanes (ego x 2, left, right), but some has 5 (changing lane). + + ## I. Functions diff --git a/PathDet/create_path/TuSimple/process_tusimple.py b/PathDet/create_path/TuSimple/process_tusimple.py index da72087..429412e 100644 --- a/PathDet/create_path/TuSimple/process_tusimple.py +++ b/PathDet/create_path/TuSimple/process_tusimple.py @@ -14,18 +14,6 @@ def custom_warning_format(message, category, filename, lineno, line=None): warnings.formatwarning = custom_warning_format -# ============================== Dataset structure ============================== # - -root_dir = "TUSimple" -train_dir = "train_set" -test_dir = "test_set" - -train_clip_codes = ["0313", "0531", "0601"] # Train labels are split into 3 dirs -test_file = "test_label.json" # Test file name - -img_width = 1280 -img_height = 720 - # ============================== Helper functions ============================== # def normalizeCoords(lane, width, height): @@ -104,16 +92,35 @@ def getDrivablePath(left_ego, right_ego): drivable_path.append((x_bottom, img_height)) # Extend drivable path to be on par with longest ego lane + # By making it parallel with longer ego lane y_top = min(left_ego[0][1], right_ego[0][1]) - if len(drivable_path) >= 2: - x1, y1 = drivable_path[0] - x2, y2 = drivable_path[1] - if (x2 == x1): - x_top = x1 - else: - a = (y2 - y1) / (x2 - x1) - x_top = x1 + (y_top - y1) / a - drivable_path.insert(0, (x_top, y_top)) + sign_left_ego = left_ego[0][0] - left_ego[1][0] + sign_right_ego = right_ego[0][0] - right_ego[1][0] + sign_val = sign_left_ego * sign_right_ego + if (sign_val > 0): # 2 egos going the same direction + longer_ego = left_ego if left_ego[0][1] < right_ego[0][1] else right_ego + if len(longer_ego) >= 2 and len(drivable_path) >= 2: + x1, y1 = longer_ego[0] + x2, y2 = longer_ego[1] + if (x2 == x1): + x_top = drivable_path[0][0] + else: + a = (y2 - y1) / (x2 - x1) + x_top = drivable_path[0][0] + (y_top - drivable_path[0][1]) / a + + drivable_path.insert(0, (x_top, y_top)) + else: + # Extend drivable path to be on par with longest ego lane + if len(drivable_path) >= 2: + x1, y1 = drivable_path[0] + x2, y2 = drivable_path[1] + if (x2 == x1): + x_top = x1 + else: + a = (y2 - y1) / (x2 - x1) + x_top = x1 + (y_top - y1) / a + + drivable_path.insert(0, (x_top, y_top)) return drivable_path @@ -138,7 +145,7 @@ def annotateGT( # Define save name # Keep original pathname (back to 5 levels) for traceability, but replace "/" with "-" # Also save in PNG (EXTREMELY SLOW compared to jpg, for lossless quality) - save_name = "_".join(anno_raw_file.split("/")[-5 : ]).replace(".jpg", ".png") + save_name = str(img_id_counter).zfill(5) + ".png" # Copy raw img and put it in raw dir. raw_img.save(os.path.join(raw_dir, save_name)) @@ -171,11 +178,11 @@ def annotateGT( # Save visualization img, same format with raw, just different dir raw_img.save(os.path.join(visualization_dir, save_name)) - # Working on binary mask. Here I reuse raw_img for robustness. - draw.rectangle([0, 0, img_width, img_height], fill = 0) - draw.line(drivable_renormed, fill = 255, width = lane_w) - raw_img.save(os.path.join(mask_dir, save_name)) - + # Working on binary mask + mask = Image.new("L", (img_width, img_height), 0) + mask_draw = ImageDraw.Draw(mask) + mask_draw.line(drivable_renormed, fill = 255, width = lane_w) + mask.save(os.path.join(mask_dir, save_name)) def parseAnnotations(anno_path): """ @@ -229,6 +236,18 @@ def parseAnnotations(anno_path): if __name__ == "__main__": + # ============================== Dataset structure ============================== # + + root_dir = "TUSimple" + train_dir = "train_set" + test_dir = "test_set" + + train_clip_codes = ["0313", "0531", "0601"] # Train labels are split into 3 dirs + test_file = "test_label.json" # Test file name + + img_width = 1280 + img_height = 720 + # ============================== Parsing args ============================== # parser = argparse.ArgumentParser( @@ -283,13 +302,22 @@ def parseAnnotations(anno_path): "data": {} } + img_id_counter = -1 + for anno_file in data_master["files"]: print(f"\n==================== Processing data in label file {anno_file} ====================\n") this_data = parseAnnotations(anno_file) - for raw_file, anno_entry in this_data.items(): + + list_raw_files = list(this_data.keys()) + for raw_file in list_raw_files: + + # Conduct index increment + img_id_counter += 1 set_dir = "/".join(anno_file.split("/")[ : -1]) # Slap "train_set" or "test_set" to the end set_dir = os.path.join(set_dir, test_dir) if test_file in anno_file else set_dir # Tricky test dir + # Annotate raw images + anno_entry = this_data[raw_file] annotateGT( anno_entry, anno_raw_file = os.path.join(set_dir, raw_file), @@ -297,11 +325,15 @@ def parseAnnotations(anno_path): visualization_dir = os.path.join(output_dir, "visualization"), mask_dir = os.path.join(output_dir, "segmentation"), img_height = anno_entry["img_height"], - img_width = anno_entry["img_width"] + img_width = anno_entry["img_width"], ) + # Pop redundant keys del anno_entry["lanes"] - del anno_entry["ego_indexes"] + del anno_entry["ego_indexes"] + # Change `raw_file` to 5-digit incremental index + this_data[str(img_id_counter).zfill(5)] = anno_entry + this_data.pop(raw_file) data_master["data"].update(this_data) print(f"Processed {len(this_data)} entries in above file.\n")

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