bit-bots · Flova · Nov 23, 2024 · Nov 23, 2024 · Nov 23, 2024 · Nov 23, 2024
diff --git a/.github/workflows/main.yml b/.github/workflows/main.yml
@@ -7,23 +7,21 @@ jobs:
     runs-on: ${{ matrix.os }}
     strategy:
         matrix:
-            os: [ubuntu-22.04, ubuntu-20.04, windows-latest]
+            os: [ubuntu-24.04, ubuntu-22.04, windows-latest]
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
+
+      - name: Install Poetry
+        run: pipx install poetry
 
       - name: Set up Python
-        uses: actions/setup-python@v1
+        uses: actions/setup-python@v4
         with:
-          python-version: 3.8
-
-      - name: Upgrade pip
-        run: python3 -m pip install --upgrade pip
-
-      - name: Install Poetry
-        run: pip3 install poetry --user
+          python-version: 3.x
+          cache: "poetry"
 
       - name: Install Dependencies
-        run: poetry install
+        run: poetry install --with dev
 
       # Prints the help pages of all scripts to see if the imports etc. work
       - name: Test the help pages

diff --git a/poetry.lock b/poetry.lock
diff --git a/pyproject.toml b/pyproject.toml
@@ -1,25 +1,25 @@
 [tool.poetry]
 name = "YOEO"
-version = "1.5.0"
+version = "1.6.0"
 description = "A hybrid CNN for object detection and semantic segmentation"
 authors = ["Florian Vahl <git@flova.de>", "Jan Gutsche <git@jagut.de>"]
 
 [tool.poetry.dependencies]
-python = ">=3.8,<4.0"
-numpy = "^1.21.1"
-torch = ">=1.10.1, <1.13.0"
-torchvision = ">=0.8.2"
+python = ">=3.10,<4.0"
 matplotlib = "^3.3.3"
-tensorboard = "^2.12.2"
 terminaltables = "^3.1.10"
-Pillow = "^9.1.0"
-tqdm = "^4.64.1"
 imgaug = "^0.4.0"
+numpy = "^1.26"
+torch = "^2.5.1"
+torchvision = "^0.20.1"
+pillow = "^11.0.0"
 torchsummary = "^1.5.1"
-PyYAML = "^6.0"
-opencv-python = "^4.5.2"
+tqdm = "^4.67.0"
+opencv-python = "^4.10.0.84"
+pyyaml = "^6.0.2"
+tensorboard = "^2.18.0"
 
-[tool.poetry.dev-dependencies]
+[tool.poetry.group.dev.dependencies]
 onnxruntime = "^1.14.0"
 profilehooks = "^1.12.0"
 onnx = "^1.9.0"

diff --git a/yoeo/test.py b/yoeo/test.py
    return metrics_output, seg_class_ious, secondary_metric


 def print_eval_stats(metrics_output: Optional[Tuple[np.ndarray]], 
                     seg_class_ious: List[np.float64], 
                     secondary_metric: Optional[Metric], 
                     class_config: ClassConfig, 
                     verbose: bool
                     ):
    # Print detection statistics
        if verbose:
            classes = class_config.get_group_class_names()
            mbACC_per_class = [secondary_metric.bACC(i) for i in range(len(classes))]
                        
            sec_table = [["Index", "Class", "bACC"]]
            for i, c in enumerate(classes):
                sec_table += [[i, c, "%.5f" % mbACC_per_class[i]]]
        # Extract labels
        labels += bb_targets[:, 1].tolist()

        # If a subset of the detection classes should be grouped into one class for non-maximum suppression and the 
        # subsequent AP-computation, we need to group those class labels here.
        if class_config.classes_should_be_grouped():
            labels = class_config.group(labels)
            )

        sample_stat, secondary_stat = get_batch_statistics(
            yolo_outputs, 
            bb_targets, 
            iou_threshold=iou_thres, 
            group_config=class_config.get_group_config()
        )

    # Concatenate sample statistics
    true_positives, pred_scores, pred_labels = [
        np.concatenate(x, 0) for x in list(zip(*sample_metrics))]
    
    yolo_metrics_output = ap_per_class(
        true_positives, pred_scores, pred_labels, labels)

diff --git a/yoeo/detect.py b/yoeo/detect.py
    :param group_config: GroupConfiguration for this model (optional, defaults to None)
    :type group_config: Optional[GroupConfig]

    :return: Detections on image with each detection in the format: [x1, y1, x2, y2, confidence, class], Segmentation as 2d numpy array with the coresponding class id in each cell
    :rtype: nd.array, nd.array
    """
    model.eval()  # Set model to evaluation mode
           conf_thres: float = 0.5, 
           nms_thres: float = 0.5,
           group_config: Optional[GroupConfig] = None
            ):
    """Inferences images with model.

    :param model: Model for inference
    """
    # Create plot
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
    plt.figure()
    fig, ax = plt.subplots(1)
    # Get segmentation
    pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape[:2])) // 2


    seg_map = seg[
                int(pad_y) : int(img_size - pad_y),
                int(pad_x) : int(img_size - pad_x),
                ] * 255


    ax.imshow(
        SegmentationMapsOnImage(
            seg[
                int(pad_y) : int(img_size - pad_y),
                int(pad_x) : int(img_size - pad_x),
                ], shape=img.shape).draw_on_image(img)[0])
    # Rescale boxes to original image
    detections = rescale_boxes(detections, img_size, img.shape[:2])
	return metrics_output, seg_class_ious, secondary_metric


	def print_eval_stats(metrics_output: Optional[Tuple[np.ndarray]],
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	seg_class_ious: List[np.float64],
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	secondary_metric: Optional[Metric],
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	class_config: ClassConfig,
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	verbose: bool
	):
	# Print detection statistics
	if verbose:
	classes = class_config.get_group_class_names()
	mbACC_per_class = [secondary_metric.bACC(i) for i in range(len(classes))]

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	sec_table = [["Index", "Class", "bACC"]]
	for i, c in enumerate(classes):
	sec_table += [[i, c, "%.5f" % mbACC_per_class[i]]]
	# Extract labels
	labels += bb_targets[:, 1].tolist()

	# If a subset of the detection classes should be grouped into one class for non-maximum suppression and the
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	# subsequent AP-computation, we need to group those class labels here.
	if class_config.classes_should_be_grouped():
	labels = class_config.group(labels)
	)

	sample_stat, secondary_stat = get_batch_statistics(
	yolo_outputs,
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	bb_targets,
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	iou_threshold=iou_thres,
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	group_config=class_config.get_group_config()
	)

	# Concatenate sample statistics
	true_positives, pred_scores, pred_labels = [
	np.concatenate(x, 0) for x in list(zip(*sample_metrics))]

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	yolo_metrics_output = ap_per_class(
	true_positives, pred_scores, pred_labels, labels)
	:param group_config: GroupConfiguration for this model (optional, defaults to None)
	:type group_config: Optional[GroupConfig]

	:return: Detections on image with each detection in the format: [x1, y1, x2, y2, confidence, class], Segmentation as 2d numpy array with the coresponding class id in each cell
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	:rtype: nd.array, nd.array
	"""
	model.eval() # Set model to evaluation mode
	conf_thres: float = 0.5,
	nms_thres: float = 0.5,
	group_config: Optional[GroupConfig] = None
	):
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	"""Inferences images with model.

	:param model: Model for inference
	"""
	# Create plot
	img = cv2.imread(image_path)
	img = cv2.cvtColor(img,cv2.COLOR_BGR2RGB)
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	plt.figure()
	fig, ax = plt.subplots(1)
	# Get segmentation
	pad_y = max(img.shape[1] - img.shape[0], 0) * (img_size / max(img.shape[:2])) // 2


	seg_map = seg[
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	int(pad_y) : int(img_size - pad_y),
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	int(pad_x) : int(img_size - pad_x),
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	] * 255


	ax.imshow(
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	SegmentationMapsOnImage(
	seg[
	int(pad_y) : int(img_size - pad_y),
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	int(pad_x) : int(img_size - pad_x),
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	], shape=img.shape).draw_on_image(img)[0])
	# Rescale boxes to original image
	detections = rescale_boxes(detections, img_size, img.shape[:2])