From 7dcccad1c9bd38a3ff9e6c8d611896c7237f02af Mon Sep 17 00:00:00 2001 From: Antonio De Lucreziis Date: Thu, 1 Aug 2024 01:29:44 +0200 Subject: [PATCH] initial commit --- .gitignore | 15 +++++ README.md | 36 ++++++++++ cv_maze.py | 161 ++++++++++++++++++++++++++++++++++++++++++++ images/IMG_0863.jpg | Bin 0 -> 2281374 bytes images/IMG_0864.jpg | Bin 0 -> 2376689 bytes images/IMG_0866.jpg | Bin 0 -> 2547594 bytes images/IMG_0867.jpg | Bin 0 -> 3597003 bytes main.py | 29 ++++++++ main_camera.py | 30 +++++++++ poetry.lock | 125 ++++++++++++++++++++++++++++++++++ pyproject.toml | 20 ++++++ utils.py | 118 ++++++++++++++++++++++++++++++++ 12 files changed, 534 insertions(+) create mode 100644 .gitignore create mode 100644 README.md create mode 100644 cv_maze.py create mode 100644 images/IMG_0863.jpg create mode 100644 images/IMG_0864.jpg create mode 100644 images/IMG_0866.jpg create mode 100644 images/IMG_0867.jpg create mode 100644 main.py create mode 100644 main_camera.py create mode 100644 poetry.lock create mode 100644 pyproject.toml create mode 100644 utils.py diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..4742a2d --- /dev/null +++ b/.gitignore @@ -0,0 +1,15 @@ +# Local files +.env +*.local* + +# Python +__pycache__/ + +# Binaries +bin/ +.out/ +out/ +dist/ + +# Editors +.vscode/ diff --git a/README.md b/README.md new file mode 100644 index 0000000..e9180cc --- /dev/null +++ b/README.md @@ -0,0 +1,36 @@ +# OpenCV Maze Solver + +

+ +

+ +This is a simple maze solver using OpenCV and Python. The maze is solved using the A* algorithm from the NetworkX library. + +## Installation + +This project uses Poetry for dependency management. To install the dependencies, run the following command: + +```bash +poetry install +``` + +## Usage + +### Image + +To run the maze solver, use the following command: + +```bash +poetry run python main.py --image +``` + +Replace `` with the path to the maze image you want to solve like `images/IMG_0867.jpg`. + + +### Camera + +To run the maze solver using the camera, use the following command: + +```bash +poetry run python main_camera.py +``` \ No newline at end of file diff --git a/cv_maze.py b/cv_maze.py new file mode 100644 index 0000000..157e60f --- /dev/null +++ b/cv_maze.py @@ -0,0 +1,161 @@ +import cv2 +import numpy as np + +import utils + + +def solve_maze(image, debug=False): + original_width, original_height = image.shape[1], image.shape[0] + image = cv2.resize(image, (4000, 3000)) + + def debug_image(label, image, *args, **kwargs): + if debug: + utils.display_image(label, image, *args, **kwargs) + + debug_image("Image", image) + + # convert the image to grayscale + image_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) + + # extract the tags + tag_registry = utils.extract_tags_dict_single(image_gray) + + missing_tags = {1, 2, 3, 4} - set(tag_registry.keys()) + if len(missing_tags) > 0: + raise Exception(f"Missing tags in given image: {missing_tags}") + + # extract the foreground + normalized_image = utils.extract_foreground(image_gray) + debug_image("Normalized", normalized_image) + + # draw a black rectangle around the detected tags + for r in tag_registry.values(): + (ptA, ptB, ptC, ptD) = r.corners + + # get min_x, min_y, max_x, max_y + min_x = min(ptA[0], ptB[0], ptC[0], ptD[0]) + min_y = min(ptA[1], ptB[1], ptC[1], ptD[1]) + max_x = max(ptA[0], ptB[0], ptC[0], ptD[0]) + max_y = max(ptA[1], ptB[1], ptC[1], ptD[1]) + + width = max_x - min_x + height = max_y - min_y + + # enlarge the rectangle + min_x -= (width / 7) * 1.5 + min_y -= (height * 2 / 7) * 1.5 + max_x += (width / 7) * 1.5 + max_y += (height / 7) * 1.5 + + # draw the rectangle + cv2.rectangle(normalized_image, (int(min_x), int(min_y)), (int(max_x), int(max_y)), (255, 255, 255), -1) + + # cv2.fillPoly(image, [np.int32(r.corners)], (0, 0, 0)) + + # erode the image + eroded = cv2.erode(normalized_image, np.ones((7, 7), np.uint8)) + # eroded = cv2.GaussianBlur(eroded, (15, 15), 0) + + debug_image("Eroded", eroded) + + # binarize the image + _, thresh = cv2.threshold(eroded, 150, 255, cv2.THRESH_BINARY) + thresh = cv2.bitwise_not(thresh) + + debug_image("Threshold", thresh) + + dilated = cv2.dilate(thresh, None, iterations=8) + + debug_image("Dilated", dilated) + + # crop the maze region using tag 1 and 2 as bounding box + tag_1_center = tag_registry[1].center + tag_2_center = tag_registry[2].center + + min_x = min(tag_1_center[0], tag_2_center[0]) + min_y = min(tag_1_center[1], tag_2_center[1]) + max_x = max(tag_1_center[0], tag_2_center[0]) + max_y = max(tag_1_center[1], tag_2_center[1]) + + def transform_1(x, y): + return x - min_x, y - min_y + + cropped_image = dilated[int(min_y) : int(max_y), int(min_x) : int(max_x)] + + cropped_image = cv2.cvtColor(cropped_image, cv2.COLOR_GRAY2BGR) + + cv2.circle(cropped_image, np.int32(transform_1(*tag_registry[3].center)), 10, (0, 0, 255), -1) + cv2.circle(cropped_image, np.int32(transform_1(*tag_registry[4].center)), 10, (0, 255, 0), -1) + + debug_image("Cropped", cropped_image) + + def transform_2(x, y): + return int((x - min_x) * 256 / (max_x - min_x)), int((y - min_y) * 256 / (max_y - min_y)) + + maze_image_small = cv2.resize(cropped_image, (256, 256), interpolation=cv2.INTER_NEAREST) + + debug_image("Maze", maze_image_small, interpolation=cv2.INTER_NEAREST) + + # convert the maze image to a numpy bitmap + + maze_bitmap = np.zeros((256, 256), dtype=np.uint8) + + for x in range(256): + for y in range(256): + if maze_image_small[y, x, 0] > 0: + maze_bitmap[y, x] = 1 + + utils.display_image("Maze Bitmap", maze_bitmap * 255, interpolation=cv2.INTER_NEAREST) + + # use networkx to solve the maze + + import networkx as nx + from networkx import Graph + + # def transform_3(x, y): + # pt = transform_2(x, y) + # return pt[0] // 2, pt[1] // 2 + + G: Graph = nx.grid_2d_graph(256, 256) + G.add_edges_from( + [((x, y), (x + 1, y + 1)) for x in range(255) for y in range(255)] + + [((x + 1, y), (x, y + 1)) for x in range(255) for y in range(255)], + weight=1.4, + ) + + for x in range(256): + for y in range(256): + if maze_bitmap[y, x] == 1: + G.remove_node((x, y)) + + start_node = transform_2(*tag_registry[3].center) + end_node = transform_2(*tag_registry[4].center) + + print(f"Start: {start_node}, End: {end_node}") + + def dist(a, b): + (x1, y1) = a + (x2, y2) = b + return ((x1 - x2) ** 2 + (y1 - y2) ** 2) ** 0.5 + + try: + path = nx.astar_path(G, start_node, end_node, heuristic=dist) + except nx.NetworkXNoPath: + raise Exception("No path found") + + print(f"Shortest path length: {len(path)}") + + # draw the path on the maze image + for x, y in path: + maze_image_small[y, x] = [0, 255, 0] + + debug_image("Maze Path", maze_image_small, interpolation=cv2.INTER_NEAREST) + + def un_transform(x, y): + out_x = int(x * (max_x - min_x) / 256 + min_x) + out_y = int(y * (max_y - min_y) / 256 + min_y) + return int(out_x * original_width / 4000), int(out_y * original_height / 3000) + + path = [un_transform(x, y) for x, y in path] + + return path diff --git a/images/IMG_0863.jpg b/images/IMG_0863.jpg new file mode 100644 index 0000000000000000000000000000000000000000..4d007c32221388f5b957af13056efc2489a54fd6 GIT binary patch literal 2281374 zcmbTd2|SeD|37@L8H}xwEh97bvXu;Ctb@vykfl|HQOYeDgsg*BjTuXdZdu9}6>ZOU#a`~T8?*Z2GSzkbj2{9ez=<-C2)=X{oPea?Qh-nBl+Gr2GD+!+84 z901Ay0B(SW1ONg?T<}v2ZT^L~!ngtw0!TQA;C}$x3OIjZ06swyfAAMb@n0TT0)+on z7X|Z;Klv`;ECL;d;Q>+uQRr zIA`nPCT)DqM_O^W-&tw*(_W{2T>Q>m@KV}fF^3=6;(v0yw(4$GJOJuy>YBQ0+PWHe zY5Z;r7$S*ql4v?1dAsf0l_rcgMKJJ$d9GaTyAU^&-IQ_R@ zcr~?j=pUKu>j0!9e!mxi%5>y!%-4Ef>|ecbJUsZv7ii;UWFr2hZ(!tqVa|Us4yJSc 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