diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..6b65053 --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,42 @@ +# .github/workflows/test.yml +name: Test + +on: + push: + branches: [ main ] + pull_request: + branches: [ main ] + +jobs: + test: + name: Test + runs-on: ubuntu-latest + strategy: + matrix: + python-version: + - "3.10" + - "3.11" + - "3.12" + + steps: + - uses: actions/checkout@v4 + + - name: Install uv and set the python version + uses: astral-sh/setup-uv@v5 + with: + version: "0.5.23" + enable-cache: true + cache-dependency-glob: "uv.lock" + python-version: ${{ matrix.python-version }} + + - name: Install the project + run: uv sync --all-groups --no-group dev-model + + - name: Checking linter and formatting + run: uvx ruff check + + - name: Run tests + run: uv run pytest tests -v + + - name: Test with Coverage + run: uv run pytest --cov=src tests/ diff --git a/.gitignore b/.gitignore index 505a3b1..cf05f26 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,8 @@ wheels/ # Virtual environments .venv +*.onnx +*.pt +*.DS_Store +.bak/ +*.coverage \ No newline at end of file diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d90d780..49cff5c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -12,7 +12,7 @@ repos: - id: trailing-whitespace # python code formatting - repo: https://github.com/charliermarsh/ruff-pre-commit - rev: v0.7.3 + rev: v0.9.2 hooks: - id: ruff types_or: [python, pyi, jupyter] @@ -25,4 +25,4 @@ repos: rev: v1.5.5 hooks: - id: forbid-crlf - - id: remove-crlf \ No newline at end of file + - id: remove-crlf diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..2885d3e --- /dev/null +++ b/Makefile @@ -0,0 +1,11 @@ +yolo-export-onnx: + yolo export \ + model=color_correction_asdfghjkl/asset/.model/yv8-det.pt \ + format=onnx \ + device=mps \ + simplify=True \ + dynamic=False \ + half=True + +test: + pytest tests -v \ No newline at end of file diff --git a/README.md b/README.md index 2964446..18f630f 100644 --- a/README.md +++ b/README.md @@ -40,7 +40,5 @@ This package is designed to perform color correction on images using the Color C - [Colour Science Python](https://www.colour-science.org/colour-checker-detection/) - [Fast and Robust Multiple ColorChecker Detection ()](https://github.com/pedrodiamel/colorchecker-detection) - [Automatic color correction with OpenCV and Python (PyImageSearch)](https://pyimagesearch.com/2021/02/15/automatic-color-correction-with-opencv-and-python/) - ---- - -Happy Color Correcting! 🌟 +- [ONNX-YOLOv8-Object-Detection](https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection) +- [yolov8-triton](https://github.com/omarabid59/yolov8-triton/tree/main) diff --git a/pyproject.toml b/pyproject.toml index 9b33e74..3c30811 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -8,6 +8,7 @@ authors = [ ] requires-python = ">=3.10" dependencies = [ + "httpx>=0.28.1", "pydantic>=2.10.5", "pydantic-settings>=2.7.1", ] @@ -24,15 +25,20 @@ analyze = [ "matplotlib>=3.10.0", ] yolodet = [ + "onnx>=1.17.0", "onnxruntime>=1.20.1", ] mccdet = [ "opencv-contrib-python>=4.11.0.86", ] dev = [ + "pytest-cov==6.0.0", "pytest>=8.3.4", "ruff>=0.9.2", ] +dev-model = [ + "ultralytics>=8.3.65", +] # ---- ruff ---- [tool.ruff] @@ -129,4 +135,4 @@ unfixable = ["F401"] [tool.ruff.format] indent-style = "space" quote-style = "double" -line-ending = "lf" \ No newline at end of file +line-ending = "lf" diff --git a/src/color_correction_asdfghjkl/constant/yolov8_det.py b/src/color_correction_asdfghjkl/constant/yolov8_det.py new file mode 100644 index 0000000..5739d34 --- /dev/null +++ b/src/color_correction_asdfghjkl/constant/yolov8_det.py @@ -0,0 +1,4 @@ +class_names = [ + "patch", + "card", +] diff --git a/src/color_correction_asdfghjkl/core/card_detection/base.py b/src/color_correction_asdfghjkl/core/card_detection/base.py new file mode 100644 index 0000000..4240e6b --- /dev/null +++ b/src/color_correction_asdfghjkl/core/card_detection/base.py @@ -0,0 +1,14 @@ +from abc import ABC, abstractmethod + +import numpy as np + +from color_correction_asdfghjkl.schemas.yolov8_det import DetectionResult + + +class BaseCardDetector(ABC): + @abstractmethod + def detect( + self, + image: np.ndarray, + conf: float = 0.15, + ) -> DetectionResult: ... diff --git a/src/color_correction_asdfghjkl/core/card_detection/yolov8_det_onnx.py b/src/color_correction_asdfghjkl/core/card_detection/yolov8_det_onnx.py new file mode 100644 index 0000000..15748d4 --- /dev/null +++ b/src/color_correction_asdfghjkl/core/card_detection/yolov8_det_onnx.py @@ -0,0 +1,194 @@ +import time + +import cv2 +import numpy as np +import onnxruntime + +from color_correction_asdfghjkl.core.card_detection.base import BaseCardDetector +from color_correction_asdfghjkl.schemas.yolov8_det import DetectionResult +from color_correction_asdfghjkl.utils.downloader import downloader_model_yolov8 +from color_correction_asdfghjkl.utils.yolo_utils import ( + multiclass_nms, + xywh2xyxy, +) + + +class YOLOv8CardDetector(BaseCardDetector): + """YOLOv8CardDetector is a class that implements card detection + using the YOLOv8 model. + + Reference + --------- + https://github.com/ibaiGorordo/ONNX-YOLOv8-Object-Detection/blob/main/yolov8/YOLOv8.py + + """ + + def __init__( + self, + conf_th: float = 0.15, + iou_th: float = 0.7, + path: str | None = None, + use_gpu: bool = False, + ) -> None: + self.conf_threshold = conf_th + self.iou_threshold = iou_th + self.use_gpu = use_gpu + if path is None: + print("Auto downloading YOLOv8 model...") + path = downloader_model_yolov8(use_gpu) + self.__initialize_model(path) + + def detect(self, image: np.ndarray) -> DetectionResult: + """ + Detect objects in the given image using YOLOv8 model. + + Parameters + ---------- + image : np.ndarray + The input image BGR in which to detect objects. + + Returns + ------- + DetectionResult + A dataclass containing detected bounding boxes, confidence scores, + and class IDs. + """ + input_tensor = self.__prepare_input(image) + outputs = self.__inference(input_tensor) + boxes, scores, class_ids = self.__process_output(outputs) + + det_res = DetectionResult( + boxes=boxes, + scores=scores, + class_ids=class_ids, + ) + + return det_res + + # Service functions + def __initialize_model(self, path: str) -> None: + self.session = onnxruntime.InferenceSession( + path, + providers=onnxruntime.get_available_providers(), + ) + # Get model info + self.__get_input_details() + self.__get_output_details() + + def __prepare_input(self, original_image: np.ndarray) -> np.ndarray: + [height, width, _] = original_image.shape + + # expected shape based on model input + expected_width = self.input_width + expected_height = self.input_height + expected_length = min((expected_height, expected_width)) + + length = max((height, width)) + # self.scale_to_expected = expected_length / length + self.scale_to_ori = length / expected_length + + image = np.zeros((length, length, 3), np.uint8) + image[0:height, 0:width] = original_image + + input_image = cv2.resize(image, (expected_width, expected_height)) + + if self.use_gpu: + input_image = (input_image / 255.0).astype(np.float16) + else: + input_image = (input_image / 255.0).astype(np.float32) + # Channel first + input_image = input_image.transpose(2, 0, 1) + + # Expand dimensions + input_image = np.expand_dims(input_image, axis=0) + return input_image + + def __inference(self, input_tensor: np.ndarray) -> list[np.ndarray]: + start = time.perf_counter() # noqa: F841 + outputs = self.session.run( + self.output_names, + {self.input_names[0]: input_tensor}, + ) + + # print(f"Inference time: {(time.perf_counter() - start) * 1000:.2f} ms") + return outputs + + def __process_output( + self, + output: list[np.ndarray], + ) -> tuple[list[list[int]], list[float], list[int]]: + predictions = np.squeeze(output[0]).T + + # Filter out object confidence scores below threshold + scores = np.max(predictions[:, 4:], axis=1) + predictions = predictions[scores > self.conf_threshold, :] + scores = scores[scores > self.conf_threshold] + + if len(scores) == 0: + return [], [], [] + + # Get the class with the highest confidence + class_ids = np.argmax(predictions[:, 4:], axis=1) + + # Get bounding boxes for each object + boxes = self.__extract_boxes(predictions) + + # Apply non-maxima suppression to suppress weak, overlapping bounding boxes + # indices = nms(boxes, scores, self.iou_threshold) + indices = multiclass_nms( + boxes, + scores, + class_ids, + self.iou_threshold, + ) + + return ( + boxes[indices].astype(int).tolist(), + scores[indices].tolist(), + class_ids[indices].tolist(), + ) + + # Helper functions + def __extract_boxes(self, predictions: np.ndarray) -> np.ndarray: + # Extract boxes from predictions + boxes = predictions[:, :4] + + # Scale boxes to original image dimensions + boxes = self.__rescale_boxes(boxes) + + # Convert boxes to xyxy format + boxes = xywh2xyxy(boxes) + + return boxes + + def __rescale_boxes(self, boxes: np.ndarray) -> np.ndarray: + # Rescale boxes to original image dimensions + boxes *= self.scale_to_ori + return boxes + + def __get_input_details(self) -> None: + model_inputs = self.session.get_inputs() + self.input_names = [model_inputs[i].name for i in range(len(model_inputs))] + + self.input_shape = model_inputs[0].shape + self.input_height = self.input_shape[2] + self.input_width = self.input_shape[3] + + def __get_output_details(self) -> None: + model_outputs = self.session.get_outputs() + self.output_names = [model_outputs[i].name for i in range(len(model_outputs))] + + +if __name__ == "__main__": + print("YOLOv8CardDetector") + model_path = "color_correction_asdfghjkl/asset/.model/yv8-det.onnx" + image_path = "color_correction_asdfghjkl/asset/images/cc-1.jpg" + image_path = "color_correction_asdfghjkl/asset/images/Test 19.png" + detector = YOLOv8CardDetector(conf_th=0.15, iou_th=0.7, use_gpu=True) + + input_image = cv2.imread(image_path) + # input_image = cv2.resize(input_image, (640, 640)) + result = detector.detect(input_image) + result.print_summary() + image_drawed = result.draw_detections(input_image) + cv2.imwrite("result.png", image_drawed) diff --git a/src/color_correction_asdfghjkl/schemas/device.py b/src/color_correction_asdfghjkl/schemas/device.py new file mode 100644 index 0000000..2df25a9 --- /dev/null +++ b/src/color_correction_asdfghjkl/schemas/device.py @@ -0,0 +1,30 @@ +from enum import Enum + +from pydantic import BaseModel, Field + + +class GPUType(str, Enum): + NVIDIA = "NVIDIA" + AMD = "AMD" + APPLE = "Apple Integrated" + UNKNOWN = "Unknown GPU" + + +class CPUArchitecture(str, Enum): + INTEL = "Intel" + AMD = "AMD" + ARM = "ARM" + APPLE = "Apple Silicon" + UNKNOWN = "Unknown" + + +class DeviceSpecs(BaseModel): + """Device specifications schema.""" + + os_name: str = Field(..., description="Operating system name") + cpu_arch: CPUArchitecture = Field( + CPUArchitecture.UNKNOWN, + description="CPU architecture", + ) + gpu_type: GPUType = Field(GPUType.UNKNOWN, description="GPU type") + is_apple_silicon: bool = Field(False, description="Whether device is Apple Silicon") diff --git a/src/color_correction_asdfghjkl/schemas/yolov8_det.py b/src/color_correction_asdfghjkl/schemas/yolov8_det.py new file mode 100644 index 0000000..c845b29 --- /dev/null +++ b/src/color_correction_asdfghjkl/schemas/yolov8_det.py @@ -0,0 +1,43 @@ +import numpy as np +from pydantic import BaseModel + +from color_correction_asdfghjkl.utils.yolo_utils import draw_detections + +box_tuple = tuple[int, int, int, int] + + +class DetectionResult(BaseModel): + boxes: list[box_tuple] + scores: list[float] + class_ids: list[int] + + def get_each_class_box(self) -> tuple[list[box_tuple], list[box_tuple]]: + """ + Return + ------ + Tuple[list[box_tuple], list[box_tuple]] + A tuple of two lists, where the first list contains the bounding boxes + of the cards and the second list contains the bounding boxes of the patches. + """ + ls_cards = [] + ls_patches = [] + for box, class_id in zip(self.boxes, self.class_ids, strict=False): + if class_id == 0: + ls_patches.append(box) + if class_id == 1: + ls_cards.append(box) + return ls_cards, ls_patches + + def print_summary(self) -> None: + ls_cards, ls_patches = self.get_each_class_box() + print(f"Number of cards detected: {len(ls_cards)}") + print(f"Number of patches detected: {len(ls_patches)}") + + def draw_detections(self, image: np.ndarray, mask_alpha: float = 0.2) -> np.ndarray: + return draw_detections( + image=image, + boxes=self.boxes, + scores=self.scores, + class_ids=self.class_ids, + mask_alpha=mask_alpha, + ) diff --git a/src/color_correction_asdfghjkl/utils/device_info.py b/src/color_correction_asdfghjkl/utils/device_info.py new file mode 100644 index 0000000..07bfe94 --- /dev/null +++ b/src/color_correction_asdfghjkl/utils/device_info.py @@ -0,0 +1,168 @@ +import platform +import subprocess +from typing import Any + +from color_correction_asdfghjkl.schemas.device import ( + CPUArchitecture, + DeviceSpecs, + GPUType, +) + + +def detect_darwin(specs: dict[str, Any]) -> dict[str, Any]: + """Detect hardware specifications for macOS (Darwin). + + Parameters + ---------- + specs : dict + Base specifications dictionary with OS information. + + Returns + ------- + dict + Updated specifications with CPU and GPU information. + + Notes + ----- + Detects: + 1. Apple Silicon vs Intel via sysctl + 2. GPU type (Apple/AMD/NVIDIA) via system_profiler + """ + try: + cpu_info = subprocess.check_output( + ["sysctl", "-n", "machdep.cpu.brand_string"], + ).decode() + specs["is_apple_silicon"] = "Apple" in cpu_info + specs["cpu_arch"] = ( + CPUArchitecture.APPLE if "Apple" in cpu_info else CPUArchitecture.INTEL + ) + except subprocess.SubprocessError: + specs["cpu_arch"] = CPUArchitecture.UNKNOWN + + try: + gpu_info = subprocess.check_output( + ["system_profiler", "SPDisplaysDataType"], + ).decode() + if "Apple M" in gpu_info: + specs["gpu_type"] = GPUType.APPLE + elif "AMD" in gpu_info: + specs["gpu_type"] = GPUType.AMD + elif "NVIDIA" in gpu_info: + specs["gpu_type"] = GPUType.NVIDIA + else: + specs["gpu_type"] = GPUType.UNKNOWN + except subprocess.SubprocessError: + specs["gpu_type"] = GPUType.UNKNOWN + + return specs + + +def detect_linux(specs: dict[str, Any]) -> dict[str, Any]: + """Detect hardware specifications for Linux. + + Parameters + ---------- + specs : dict + Base specifications dictionary with OS information. + + Returns + ------- + dict + Updated specifications with CPU and GPU information. + + Notes + ----- + Detects: + 1. CPU architecture (ARM/x86_64) via lscpu + 2. GPU type (NVIDIA/AMD) via nvidia-smi or lspci + """ + try: + cpu_info = subprocess.check_output("lscpu", shell=True).decode().lower() + specs["cpu_arch"] = ( + CPUArchitecture.ARM if "aarch64" in cpu_info else CPUArchitecture.INTEL + ) + except subprocess.SubprocessError: + specs["cpu_arch"] = CPUArchitecture(platform.machine()) + + # GPU detection + try: + subprocess.check_output("nvidia-smi", shell=True) + specs["gpu_type"] = GPUType.NVIDIA + except subprocess.SubprocessError: + try: + amd_info = ( + subprocess.check_output( + "lspci | grep -i amd", + shell=True, + ) + .decode() + .lower() + ) + specs["gpu_type"] = ( + GPUType.AMD + if ("vga" in amd_info or "amd" in amd_info) + else GPUType.UNKNOWN + ) + except subprocess.SubprocessError: + specs["gpu_type"] = GPUType.UNKNOWN + + return specs + + +def detect_windows(specs: dict[str, Any]) -> dict[str, Any]: + """Detect hardware specifications for Windows. + + Parameters + ---------- + specs : dict + Base specifications dictionary with OS information. + + Returns + ------- + dict + Updated specifications with CPU and GPU information. + """ + proc = platform.processor().lower() + if "intel" in proc: + specs["cpu_arch"] = CPUArchitecture.INTEL + elif "amd" in proc: + specs["cpu_arch"] = CPUArchitecture.AMD + elif "arm" in proc: + specs["cpu_arch"] = CPUArchitecture.ARM + else: + specs["cpu_arch"] = CPUArchitecture.UNKNOWN + + specs["gpu_type"] = GPUType.UNKNOWN + return specs + + +def get_device_specs() -> DeviceSpecs: + """Get device hardware specifications. + + Returns + ------- + DeviceSpecs + Pydantic model containing device specifications. + """ + specs = { + "os_name": platform.system(), + "cpu_arch": CPUArchitecture.UNKNOWN, + "gpu_type": GPUType.UNKNOWN, + "is_apple_silicon": False, + } + + detector_map = { + "Darwin": detect_darwin, + "Linux": detect_linux, + "Windows": detect_windows, + } + + detect_func = detector_map.get(specs["os_name"], lambda x: x) + specs = detect_func(specs) + + return DeviceSpecs(**specs) + + +if __name__ == "__main__": + specs = get_device_specs() + print(f"Detected specs: {specs.model_dump()}") diff --git a/src/color_correction_asdfghjkl/utils/downloader.py b/src/color_correction_asdfghjkl/utils/downloader.py new file mode 100644 index 0000000..4d7436c --- /dev/null +++ b/src/color_correction_asdfghjkl/utils/downloader.py @@ -0,0 +1,90 @@ +import os +import re +from typing import Final + +import httpx + +from color_correction_asdfghjkl.schemas.device import GPUType +from color_correction_asdfghjkl.utils.device_info import get_device_specs + + +def download_google_drive_file(file_id: str, output_file: str) -> None: + """ + Download a file from Google Drive using its file ID. + + Parameters + ---------- + file_id : str + The unique ID of the file on Google Drive. + output_file : str + The name of the output file where the content will be saved. + + Returns + ------- + None + Downloads the file and saves it locally. + """ + url: Final = f"https://drive.google.com/uc?export=download&id={file_id}" + + with httpx.Client(follow_redirects=True, timeout=60) as client: + print(f"Start downloading file from: {url}") + # First request to get confirmation token + response = client.get(url) + print(f"Response status code: {response.status_code}") + if response.status_code == 303 or "download_warning" in response.text: + # Handle large file confirmation + print("Large file confirmation needed if file is not downloaded.") + confirm_token = re.findall(r"confirm=([^&]+)", response.url.query) + print(f"Confirm token: {confirm_token}") + if confirm_token: + url = f"{url}&confirm={confirm_token[0]}" + print(f"New URL: {url}") + + # Stream download + with client.stream("GET", url, timeout=60) as response: + print(f"Downloading file to: {output_file}") + response.raise_for_status() + with open(output_file, "wb") as f: + for chunk in response.iter_bytes(): + f.write(chunk) + print(f"File downloaded: {output_file}") + + +def downloader_model_yolov8(use_gpu: bool = False) -> str: + specs = get_device_specs() + model_folder = "color_correction_asdfghjkl/asset/.model" + if use_gpu: + if specs.is_apple_silicon: + print("Apple Silicon device detected.") + fileid = "19O02x_Co2ceBQHHNUib-aVp0cjgG9V-r" + filename = "yv8-det-mps.onnx" + elif specs.gpu_type == GPUType.NVIDIA: + print("NVIDIA GPU detected.") + fileid = "19O02x_Co2ceBQHHNUib-aVp0cjgG9V-r" + filename = "yv8-det-gpu.onnx" + else: + raise ValueError( + "GPU not detected or not supported. " + "Please use CPU device. Device Info: ", + specs, + ) + else: + print("CPU device detected.") + fileid = "1d1p2HCltiJeVGi6NtDanJvqLbzXs8NLP" + filename = "yv8-det-cpu.onnx" + + os.makedirs(model_folder, exist_ok=True) + fullpath = os.path.join(model_folder, filename) + if os.path.exists(fullpath): + return fullpath + + download_google_drive_file(fileid, fullpath) + return fullpath + + +if __name__ == "__main__": + # Example usage + # file_id = "19O02x_Co2ceBQHHNUib-aVp0cjgG9V-r" + # output_file = "yv8-det-mps.onnx" + filename = downloader_model_yolov8() + print(f"Output file name: {filename}") diff --git a/src/color_correction_asdfghjkl/utils/yolo_utils.py b/src/color_correction_asdfghjkl/utils/yolo_utils.py new file mode 100644 index 0000000..e8b8044 --- /dev/null +++ b/src/color_correction_asdfghjkl/utils/yolo_utils.py @@ -0,0 +1,344 @@ +"""YOLOv8 utility functions for object detection tasks. + +This module provides utility functions for processing YOLOv8 detections, including: +- Non-maximum suppression (NMS) +- Bounding box conversions +- Drawing functions for visualization +""" + +import cv2 +import numpy as np + +from color_correction_asdfghjkl.constant.yolov8_det import class_names + +# Constants +RANDOM_SEED = 3 +DEFAULT_COLOR = (0, 0, 255) +DEFAULT_THICKNESS = 2 +DEFAULT_MASK_ALPHA = 0.3 + +# Initialize colors for visualization +rng = np.random.default_rng(RANDOM_SEED) +COLORS = rng.uniform(0, 255, size=(len(class_names), 3)) + + +# Detection Processing Functions +def nms(boxes: np.ndarray, scores: np.ndarray, iou_threshold: float) -> list[int]: + """Apply Non-Maximum Suppression (NMS) to filter overlapping bounding boxes. + + NMS is used to eliminate redundant bounding boxes in object detection tasks. + It selects the bounding boxes with the highest confidence scores while + removing boxes that overlap significantly with them. + + Logic: + - No Overlap (IoU = 0): + Boxes are retained as they do not affect each other. + - High Overlap (IoU > threshold): + The box with the lower confidence score is removed, as it is considered + a duplicate detection of the same object. + - Low Overlap (IoU < threshold): + Both boxes are retained, as they are considered detections of different objects. + + Parameters + ---------- + boxes : np.ndarray + Array of bounding boxes in format (x1, y1, x2, y2). + scores : np.ndarray + Array of confidence scores for each box. + iou_threshold : float + IoU threshold for filtering overlapping boxes. + + Returns + ------- + list[int] + Indices of the bounding boxes to keep after applying NMS. + """ + # Sort by score + sorted_indices = np.argsort(scores)[::-1] + keep_boxes = [] + + while sorted_indices.size > 0: + # Pick the last box + box_id = sorted_indices[0] + keep_boxes.append(box_id) + + # if just only one box, then break + if sorted_indices.size == 1: + break + + # Compute IoU of the picked box with the rest + ious = compute_iou(boxes[box_id, :], boxes[sorted_indices[1:], :]) + + # Remove boxes with IoU over the threshold, + print("ious:", ious) + keep_indices = np.where(ious < iou_threshold)[0] + + # update sorted_indices + sorted_indices = sorted_indices[1:][keep_indices] + + return keep_boxes + + +def multiclass_nms( + boxes: np.ndarray, + scores: np.ndarray, + class_ids: np.ndarray, + iou_threshold: float, +) -> list[int]: + """Apply non-maximum suppression to boxes across multiple classes. + + Parameters + ---------- + boxes : np.ndarray + Array of bounding boxes in format (x1, y1, x2, y2). + scores : np.ndarray + Array of confidence scores for each box. + class_ids : np.ndarray + Array of class IDs for each box. + iou_threshold : float + IoU threshold for filtering overlapping boxes. + + Returns + ------- + list[int] + Indices of boxes to keep after multiclass NMS. + """ + unique_class_ids = np.unique(class_ids) + + keep_boxes = [] + for class_id in unique_class_ids: + class_indices = np.where(class_ids == class_id)[0] + class_boxes = boxes[class_indices, :] + class_scores = scores[class_indices] + + class_keep_boxes = nms(class_boxes, class_scores, iou_threshold) + keep_boxes.extend(class_indices[class_keep_boxes]) + return keep_boxes + + +def compute_iou(box: np.ndarray, boxes: np.ndarray) -> np.ndarray: + """Compute Intersection over Union between a box and an array of boxes. + + Parameters + ---------- + box : np.ndarray + Single box in format (x1, y1, x2, y2). + boxes : np.ndarray + Array of boxes to compute IoU with. + + Returns + ------- + np.ndarray + Array of IoU values between the input box and each box in boxes. + """ + box = box.astype(np.float32) + boxes = boxes.astype(np.float32) + + # Compute xmin, ymin, xmax, ymax for both boxes + xmin = np.maximum(box[0], boxes[:, 0]) + ymin = np.maximum(box[1], boxes[:, 1]) + xmax = np.minimum(box[2], boxes[:, 2]) + ymax = np.minimum(box[3], boxes[:, 3]) + + # Compute intersection area + intersection_area = np.maximum(0, xmax - xmin) * np.maximum(0, ymax - ymin) + + # Compute union area + box_area = (box[2] - box[0]) * (box[3] - box[1]) + boxes_area = (boxes[:, 2] - boxes[:, 0]) * (boxes[:, 3] - boxes[:, 1]) + union_area = box_area + boxes_area - intersection_area + + # Compute IoU + iou = intersection_area / union_area + return iou + + +def xywh2xyxy(x: np.ndarray) -> np.ndarray: + """Convert bounding box format from (x, y, w, h) to (x1, y1, x2, y2). + + Parameters + ---------- + x : np.ndarray + Array of boxes in (x, y, w, h) format. + + Returns + ------- + np.ndarray + Array of boxes in (x1, y1, x2, y2) format. + """ + # Convert bounding box (x, y, w, h) to bounding box (x1, y1, x2, y2) + y = np.copy(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 + y[..., 1] = x[..., 1] - x[..., 3] / 2 + y[..., 2] = x[..., 0] + x[..., 2] / 2 + y[..., 3] = x[..., 1] + x[..., 3] / 2 + return y + + +# Visualization Functions +def draw_detections( + image: np.ndarray, + boxes: list[list[int]], + scores: list[float], + class_ids: list[int], + mask_alpha: float = DEFAULT_MASK_ALPHA, +) -> np.ndarray: + """Draw detection boxes, labels and masks on the image. + + Parameters + ---------- + image : np.ndarray + Input image to draw on. + boxes : list[list[int]] + List of bounding boxes in format (x1, y1, x2, y2). + scores : list[float] + List of confidence scores for each detection. + class_ids : list[int] + List of class IDs for each detection. + mask_alpha : float, optional + Transparency of the mask overlay, by default DEFAULT_MASK_ALPHA. + + Returns + ------- + np.ndarray + Image with drawn detections. + """ + det_img = image.copy() + img_height, img_width = image.shape[:2] + + # Calculate font and text properties based on image size + font_size = min([img_height, img_width]) * 0.0005 + text_thickness = int(min([img_height, img_width]) * 0.001) + + # Draw masks first (background layer) + det_img = draw_masks(det_img, boxes, class_ids, mask_alpha) + + # Draw boxes and labels (foreground layer) + for class_id, box, score in zip(class_ids, boxes, scores, strict=False): + color = COLORS[class_id] + draw_box(det_img, box, color) + + # Create and draw label + label = f"{class_names[class_id]} {int(score * 100)}%" + draw_text(det_img, label, box, color, font_size, text_thickness) + + return det_img + + +def draw_box( + image: np.ndarray, + box: list[int], + color: tuple[int, int, int] = DEFAULT_COLOR, + thickness: int = DEFAULT_THICKNESS, +) -> np.ndarray: + """Draw a bounding box on the image. + + Parameters + ---------- + image : np.ndarray + Input image to draw on. + box : list[int] + Bounding box coordinates in format (x1, y1, x2, y2). + color : tuple[int, int, int], optional + RGB color for the box, by default DEFAULT_COLOR. + thickness : int, optional + Line thickness of the box, by default DEFAULT_THICKNESS. + + Returns + ------- + np.ndarray + Image with drawn box. + """ + x1, y1, x2, y2 = box + return cv2.rectangle(image, (x1, y1), (x2, y2), color, thickness) + + +def draw_text( + image: np.ndarray, + text: str, + box: list[int], + color: tuple[int, int, int] = DEFAULT_COLOR, + font_size: float = 0.001, + text_thickness: int = 2, +) -> np.ndarray: + """Draw text with background on the image. + + Parameters + ---------- + image : np.ndarray + Input image to draw on. + text : str + Text to be drawn. + box : list[int] + Bounding box coordinates where text will be placed. + color : tuple[int, int, int], optional + RGB color for text background, by default DEFAULT_COLOR. + font_size : float, optional + Size of the font, by default 0.001. + text_thickness : int, optional + Thickness of the text, by default 2. + + Returns + ------- + np.ndarray + Image with drawn text. + """ + x1, y1, x2, y2 = box + (tw, th), _ = cv2.getTextSize( + text=text, + fontFace=cv2.FONT_HERSHEY_SIMPLEX, + fontScale=font_size, + thickness=text_thickness, + ) + th = int(th * 1.2) + + cv2.rectangle(image, (x1, y1), (x1 + tw, y1 - th), color, -1) + + return cv2.putText( + image, + text, + (x1, y1), + cv2.FONT_HERSHEY_SIMPLEX, + font_size, + (255, 255, 255), + text_thickness, + cv2.LINE_AA, + ) + + +def draw_masks( + image: np.ndarray, + boxes: list[list[int]], + classes: list[int], + mask_alpha: float = DEFAULT_MASK_ALPHA, +) -> np.ndarray: + """Draw semi-transparent masks for detection boxes. + + Parameters + ---------- + image : np.ndarray + Input image to draw on. + boxes : list[list[int]] + List of bounding boxes in format (x1, y1, x2, y2). + classes : list[int] + List of class IDs for each box. + mask_alpha : float, optional + Transparency of the mask overlay, by default DEFAULT_MASK_ALPHA. + + Returns + ------- + np.ndarray + Image with drawn masks. + """ + mask_img = image.copy() + + # Draw bounding boxes and labels of detections + for box, class_id in zip(boxes, classes, strict=False): + color = COLORS[class_id] + + x1, y1, x2, y2 = box + + # Draw fill rectangle in mask image + cv2.rectangle(mask_img, (x1, y1), (x2, y2), color, -1) + + return cv2.addWeighted(mask_img, mask_alpha, image, 1 - mask_alpha, 0) diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..99000e8 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,7 @@ +import numpy as np +import pytest + + +@pytest.fixture +def sample_image() -> np.ndarray: + return np.zeros((640, 640, 3), dtype=np.uint8) diff --git a/tests/core/__init__.py b/tests/core/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/core/card_detection/__init__.py b/tests/core/card_detection/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/tests/core/card_detection/test_yolov8_detector.py b/tests/core/card_detection/test_yolov8_detector.py new file mode 100644 index 0000000..4f4589e --- /dev/null +++ b/tests/core/card_detection/test_yolov8_detector.py @@ -0,0 +1,14 @@ +import numpy as np +import pytest + +from color_correction_asdfghjkl.core.card_detection.yolov8_det_onnx import ( + YOLOv8CardDetector, +) + + +@pytest.mark.skip(reason="Test is not implemented") +def test_detector_init(sample_image: np.ndarray) -> None: + detector = YOLOv8CardDetector(use_gpu=False) + result = detector.detect(sample_image) + assert result is not None + assert len(result.boxes) == 0 # Expect no detections on empty image diff --git a/tests/utils/test_yolo_utils.py b/tests/utils/test_yolo_utils.py new file mode 100644 index 0000000..a9b45b5 --- /dev/null +++ b/tests/utils/test_yolo_utils.py @@ -0,0 +1,285 @@ +import numpy as np +import pytest + +from color_correction_asdfghjkl.utils.yolo_utils import ( + compute_iou, + multiclass_nms, + nms, + xywh2xyxy, +) + + +# ------------------- Test for nms --------------------------- +@pytest.mark.parametrize( + "boxes,scores,iou_threshold,expected_len", + [ + # 1. Happy path - 2 overlapping boxes and 1 separate box + ( + np.array( + [[0, 0, 10, 10], [9, 9, 30, 30], [1, 1, 11, 11]], + dtype=np.float32, + ), # noqa: E501 + np.array([0.9, 0.8, 0.7]), + 0.5, + 2, + ), + # 2. Edge case - single box + ( + np.array([[0, 0, 10, 10]], dtype=np.float32), + np.array([0.9]), + 0.5, + 1, + ), + # 3. Edge case - no overlapping boxes + ( + np.array( + [[0, 0, 10, 10], [20, 20, 30, 30], [40, 40, 50, 50]], + dtype=np.float32, + ), # noqa: E501 + np.array([0.9, 0.8, 0.7]), + 0.5, + 3, + ), + # 4. Edge case - all boxes overlap significantly + ( + np.array( + [[0, 0, 10, 10], [1, 1, 11, 11], [2, 2, 10, 10]], + dtype=np.float32, + ), + np.array([0.9, 0.8, 0.7]), + 0.5, + 1, + ), + # 5. Edge case - very low IoU threshold + ( + np.array([[0, 0, 10, 10], [2, 2, 12, 12]], dtype=np.float32), + np.array([0.9, 0.8]), + 0.1, + 1, + ), + # 6. Edge case - very high IoU threshold + ( + np.array([[0, 0, 10, 10], [2, 2, 12, 12]], dtype=np.float32), + np.array([0.9, 0.8]), + 0.9, + 2, + ), + ], +) +def test_nms_parametrized( + boxes: np.ndarray, + scores: np.ndarray, + iou_threshold: float, + expected_len: int, +) -> None: + keep_indices = nms(boxes, scores, iou_threshold) + assert len(keep_indices) == expected_len + assert 0 in keep_indices # Highest scoring box should always be kept + assert all( + scores[i] >= scores[j] + for i, j in zip(keep_indices[:-1], keep_indices[1:], strict=False) + ) # Scores should be descending + + +def test_nms_empty() -> None: + empty_boxes = np.array([], dtype=np.float32).reshape(0, 4) + empty_scores = np.array([], dtype=np.float32) + keep_indices = nms(boxes=empty_boxes, scores=empty_scores, iou_threshold=0.5) + assert len(keep_indices) == 0 + + +def test_nms_same_score() -> None: + boxes = np.array([[0, 0, 10, 10], [20, 20, 30, 30]], dtype=np.float32) + scores = np.array([0.9, 0.9]) + keep_indices = nms(boxes=boxes, scores=scores, iou_threshold=0.5) + assert len(keep_indices) == 2 + + +# ------------------- Test for compute_iou ------------------- +@pytest.mark.parametrize( + "box,boxes,expected_ious", + [ + # No overlap case + ( + np.array([0, 0, 10, 10]), + np.array([[20, 20, 30, 30], [40, 40, 50, 50]]), + np.array([0.0, 0.0]), + ), + # Partial overlap case + ( + np.array([0, 0, 20, 20]), + np.array([[10, 10, 30, 30]]), + np.array([0.14285714]), + ), + # Complete overlap (identical boxes) + ( + np.array([0, 0, 10, 10]), + np.array([[0, 0, 10, 10]]), + np.array([1.0]), + ), + # Multiple boxes with varying overlap + ( + np.array([0, 0, 10, 10]), + np.array([[5, 5, 15, 15], [0, 0, 5, 5], [20, 20, 30, 30]]), + np.array([0.14285714, 0.25, 0.0]), + ), + ], +) +def test_compute_iou( + box: np.ndarray, + boxes: np.ndarray, + expected_ious: np.ndarray, +) -> None: + ious = compute_iou(box, boxes) + np.testing.assert_array_almost_equal(ious, expected_ious, decimal=7) + + +def test_compute_iou_empty_boxes() -> None: + box = np.array([0, 0, 10, 10]) + empty_boxes = np.array([], dtype=np.float32).reshape(0, 4) + ious = compute_iou(box, empty_boxes) + assert len(ious) == 0 + + +# ------------------- Test for multiclass_nms ------------------- +@pytest.mark.parametrize( + "boxes,scores,class_ids,iou_threshold,expected_len", + [ + # 1. Happy path - multiple classes with some overlapping boxes + ( + np.array( + [[0, 0, 10, 10], [1, 1, 11, 11], [20, 20, 30, 30]], + dtype=np.float32, + ), + np.array([0.9, 0.8, 0.7]), + np.array([0, 0, 1]), + 0.5, + 2, + ), + # 2. Edge case - single class + ( + np.array([[0, 0, 10, 10], [1, 1, 11, 11]], dtype=np.float32), + np.array([0.9, 0.8]), + np.array([0, 0]), + 0.5, + 1, + ), + # 3. Edge case - no overlapping boxes, different classes + ( + np.array( + [[0, 0, 10, 10], [20, 20, 30, 30], [40, 40, 50, 50]], + dtype=np.float32, + ), + np.array([0.9, 0.8, 0.7]), + np.array([0, 1, 2]), + 0.5, + 3, + ), + # 4. Edge case - multiple overlapping boxes with same class + ( + np.array( + [[0, 0, 10, 10], [1, 1, 11, 11], [2, 2, 10, 10]], + dtype=np.float32, + ), + np.array([0.9, 0.8, 0.7]), + np.array([0, 0, 0]), + 0.5, + 1, + ), + # 5. Edge case - multiple overlapping boxes with different classes + ( + np.array( + [[0, 0, 10, 10], [1, 1, 11, 11], [2, 2, 12, 12]], + dtype=np.float32, + ), + np.array([0.9, 0.8, 0.7]), + np.array([0, 1, 2]), + 0.5, + 3, + ), + ], +) +def test_multiclass_nms_parametrized( + boxes: np.ndarray, + scores: np.ndarray, + class_ids: np.ndarray, + iou_threshold: float, + expected_len: int, +) -> None: + keep_indices = multiclass_nms(boxes, scores, class_ids, iou_threshold) + assert len(keep_indices) == expected_len + + # Check that boxes with highest scores per class are kept + unique_classes = np.unique(class_ids) + for class_id in unique_classes: + class_mask = class_ids == class_id + class_scores = scores[class_mask] + if len(class_scores) > 0: + max_score_idx = np.where(class_mask)[0][np.argmax(class_scores)] + assert max_score_idx in keep_indices + + +def test_multiclass_nms_empty() -> None: + empty_boxes = np.array([], dtype=np.float32).reshape(0, 4) + empty_scores = np.array([], dtype=np.float32) + empty_class_ids = np.array([], dtype=np.int32) + keep_indices = multiclass_nms( + boxes=empty_boxes, + scores=empty_scores, + class_ids=empty_class_ids, + iou_threshold=0.5, + ) + assert len(keep_indices) == 0 + + +def test_multiclass_nms_same_score_diff_class() -> None: + boxes = np.array([[0, 0, 10, 10], [20, 20, 30, 30]], dtype=np.float32) + scores = np.array([0.9, 0.9]) + class_ids = np.array([0, 1]) + keep_indices = multiclass_nms( + boxes=boxes, + scores=scores, + class_ids=class_ids, + iou_threshold=0.5, + ) + assert len(keep_indices) == 2 + + +# ------------------- Test for xywh2xyxy ------------------- +@pytest.mark.parametrize( + "input_boxes,expected_boxes", + [ + # Single box test case + ( + np.array([[10, 10, 20, 20]], dtype=np.float32), + np.array([[0, 0, 20, 20]], dtype=np.float32), + ), + # Multiple boxes test case + ( + np.array([[10, 10, 20, 20], [30, 30, 40, 40]], dtype=np.float32), + np.array([[0, 0, 20, 20], [10, 10, 50, 50]], dtype=np.float32), + ), + # Test with decimal values + ( + np.array([[5.5, 5.5, 3, 3]], dtype=np.float32), + np.array([[4, 4, 7, 7]], dtype=np.float32), + ), + # Test with zero width/height + ( + np.array([[10, 10, 0, 0]], dtype=np.float32), + np.array([[10, 10, 10, 10]], dtype=np.float32), + ), + ], +) +def test_xywh2xyxy_parametrized( + input_boxes: np.ndarray, + expected_boxes: np.ndarray, +) -> None: + output_boxes = xywh2xyxy(input_boxes) + np.testing.assert_array_almost_equal(output_boxes, expected_boxes) + + +def test_xywh2xyxy_empty() -> None: + empty_boxes = np.array([], dtype=np.float32).reshape(0, 4) + output = xywh2xyxy(empty_boxes) + assert output.shape == (0, 4) diff --git a/uv.lock b/uv.lock index 04ede07..cc92f0a 100644 --- a/uv.lock +++ b/uv.lock @@ -1,15 +1,33 @@ version = 1 requires-python = ">=3.10" resolution-markers = [ - "python_full_version < '3.11' and platform_system == 'Darwin'", - "python_full_version < '3.11' and platform_machine == 'aarch64' and platform_system == 'Linux'", 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