pull/210/head
Kye
1 year ago
parent
df59d3642e
commit
a443666501
@ -0,0 +1,291 @@
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import cv2
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import numpy as np
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from PIL import Image
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from transformers import SamImageProcessor, SamModel, SamProcessor, pipeline
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try:
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import cv2
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import supervision as sv
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except ImportError:
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print("Please install supervision and cv")
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from enum import Enum
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class FeatureType(Enum):
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"""
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An enumeration to represent the types of features for mask adjustment in image
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segmentation.
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"""
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ISLAND = "ISLAND"
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HOLE = "HOLE"
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@classmethod
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def list(cls):
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return list(map(lambda c: c.value, cls))
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def compute_mask_iou_vectorized(masks: np.ndarray) -> np.ndarray:
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"""
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Vectorized computation of the Intersection over Union (IoU) for all pairs of masks.
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Parameters:
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masks (np.ndarray): A 3D numpy array with shape `(N, H, W)`, where `N` is the
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number of masks, `H` is the height, and `W` is the width.
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Returns:
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np.ndarray: A 2D numpy array of shape `(N, N)` where each element `[i, j]` is
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the IoU between masks `i` and `j`.
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Raises:
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ValueError: If any of the masks is found to be empty.
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"""
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if np.any(masks.sum(axis=(1, 2)) == 0):
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raise ValueError(
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"One or more masks are empty. Please filter out empty masks before"
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" using `compute_iou_vectorized` function."
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)
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masks_bool = masks.astype(bool)
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masks_flat = masks_bool.reshape(masks.shape[0], -1)
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intersection = np.logical_and(masks_flat[:, None], masks_flat[None, :]).sum(
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axis=2
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)
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union = np.logical_or(masks_flat[:, None], masks_flat[None, :]).sum(axis=2)
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iou_matrix = intersection / union
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return iou_matrix
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def mask_non_max_suppression(
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masks: np.ndarray, iou_threshold: float = 0.6
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) -> np.ndarray:
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"""
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Performs Non-Max Suppression on a set of masks by prioritizing larger masks and
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removing smaller masks that overlap significantly.
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When the IoU between two masks exceeds the specified threshold, the smaller mask
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(in terms of area) is discarded. This process is repeated for each pair of masks,
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effectively filtering out masks that are significantly overlapped by larger ones.
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Parameters:
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masks (np.ndarray): A 3D numpy array with shape `(N, H, W)`, where `N` is the
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number of masks, `H` is the height, and `W` is the width.
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iou_threshold (float): The IoU threshold for determining significant overlap.
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Returns:
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np.ndarray: A 3D numpy array of filtered masks.
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"""
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num_masks = masks.shape[0]
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areas = masks.sum(axis=(1, 2))
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sorted_idx = np.argsort(-areas)
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keep_mask = np.ones(num_masks, dtype=bool)
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iou_matrix = compute_mask_iou_vectorized(masks)
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for i in range(num_masks):
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if not keep_mask[sorted_idx[i]]:
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continue
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overlapping_masks = iou_matrix[sorted_idx[i]] > iou_threshold
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overlapping_masks[sorted_idx[i]] = False
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keep_mask[sorted_idx] = np.logical_and(
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keep_mask[sorted_idx], ~overlapping_masks
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)
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return masks[keep_mask]
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def filter_masks_by_relative_area(
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masks: np.ndarray, minimum_area: float = 0.01, maximum_area: float = 1.0
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) -> np.ndarray:
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"""
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Filters masks based on their relative area within the total area of each mask.
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Parameters:
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masks (np.ndarray): A 3D numpy array with shape `(N, H, W)`, where `N` is the
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number of masks, `H` is the height, and `W` is the width.
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minimum_area (float): The minimum relative area threshold. Must be between `0`
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and `1`.
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maximum_area (float): The maximum relative area threshold. Must be between `0`
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and `1`.
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Returns:
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np.ndarray: A 3D numpy array containing masks that fall within the specified
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relative area range.
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Raises:
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ValueError: If `minimum_area` or `maximum_area` are outside the `0` to `1`
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range, or if `minimum_area` is greater than `maximum_area`.
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"""
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if not (isinstance(masks, np.ndarray) and masks.ndim == 3):
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raise ValueError("Input must be a 3D numpy array.")
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if not (0 <= minimum_area <= 1) or not (0 <= maximum_area <= 1):
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raise ValueError(
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"`minimum_area` and `maximum_area` must be between 0 and 1."
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)
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if minimum_area > maximum_area:
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raise ValueError(
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"`minimum_area` must be less than or equal to `maximum_area`."
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)
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total_area = masks.shape[1] * masks.shape[2]
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relative_areas = masks.sum(axis=(1, 2)) / total_area
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return masks[
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(relative_areas >= minimum_area) & (relative_areas <= maximum_area)
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]
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def adjust_mask_features_by_relative_area(
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mask: np.ndarray,
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area_threshold: float,
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feature_type: FeatureType = FeatureType.ISLAND,
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) -> np.ndarray:
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"""
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Adjusts a mask by removing small islands or filling small holes based on a relative
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area threshold.
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!!! warning
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Running this function on a mask with small islands may result in empty masks.
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Parameters:
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mask (np.ndarray): A 2D numpy array with shape `(H, W)`, where `H` is the
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height, and `W` is the width.
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area_threshold (float): Threshold for relative area to remove or fill features.
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feature_type (FeatureType): Type of feature to adjust (`ISLAND` for removing
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islands, `HOLE` for filling holes).
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Returns:
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np.ndarray: A 2D numpy array containing mask.
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"""
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height, width = mask.shape
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total_area = width * height
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mask = np.uint8(mask * 255)
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operation = (
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cv2.RETR_EXTERNAL
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if feature_type == FeatureType.ISLAND
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else cv2.RETR_CCOMP
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)
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contours, _ = cv2.findContours(mask, operation, cv2.CHAIN_APPROX_SIMPLE)
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for contour in contours:
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area = cv2.contourArea(contour)
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relative_area = area / total_area
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if relative_area < area_threshold:
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cv2.drawContours(
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image=mask,
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contours=[contour],
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contourIdx=-1,
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color=(0 if feature_type == FeatureType.ISLAND else 255),
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thickness=-1,
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)
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return np.where(mask > 0, 1, 0).astype(bool)
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def masks_to_marks(masks: np.ndarray) -> sv.Detections:
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"""
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Converts a set of masks to a marks (sv.Detections) object.
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Parameters:
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masks (np.ndarray): A 3D numpy array with shape `(N, H, W)`, where `N` is the
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number of masks, `H` is the height, and `W` is the width.
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Returns:
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sv.Detections: An object containing the masks and their bounding box
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coordinates.
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"""
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return sv.Detections(mask=masks, xyxy=sv.mask_to_xyxy(masks=masks))
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def refine_marks(
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marks: sv.Detections,
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maximum_hole_area: float = 0.01,
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maximum_island_area: float = 0.01,
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minimum_mask_area: float = 0.02,
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maximum_mask_area: float = 1.0,
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) -> sv.Detections:
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"""
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Refines a set of masks by removing small islands and holes, and filtering by mask
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area.
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Parameters:
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marks (sv.Detections): An object containing the masks and their bounding box
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coordinates.
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maximum_hole_area (float): The maximum relative area of holes to be filled in
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each mask.
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maximum_island_area (float): The maximum relative area of islands to be removed
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from each mask.
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minimum_mask_area (float): The minimum relative area for a mask to be retained.
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maximum_mask_area (float): The maximum relative area for a mask to be retained.
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Returns:
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sv.Detections: An object containing the masks and their bounding box
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coordinates.
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"""
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result_masks = []
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for mask in marks.mask:
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mask = adjust_mask_features_by_relative_area(
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mask=mask,
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area_threshold=maximum_island_area,
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feature_type=FeatureType.ISLAND,
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)
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mask = adjust_mask_features_by_relative_area(
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mask=mask,
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area_threshold=maximum_hole_area,
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feature_type=FeatureType.HOLE,
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)
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if np.any(mask):
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result_masks.append(mask)
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result_masks = np.array(result_masks)
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result_masks = filter_masks_by_relative_area(
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masks=result_masks,
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minimum_area=minimum_mask_area,
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maximum_area=maximum_mask_area,
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)
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return sv.Detections(
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mask=result_masks, xyxy=sv.mask_to_xyxy(masks=result_masks)
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)
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class SegmentAnythingMarkGenerator:
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"""
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A class for performing image segmentation using a specified model.
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Parameters:
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device (str): The device to run the model on (e.g., 'cpu', 'cuda').
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model_name (str): The name of the model to be loaded. Defaults to
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'facebook/sam-vit-huge'.
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"""
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def __init__(
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self, device: str = "cpu", model_name: str = "facebook/sam-vit-huge"
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):
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self.model = SamModel.from_pretrained(model_name).to(device)
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self.processor = SamProcessor.from_pretrained(model_name)
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self.image_processor = SamImageProcessor.from_pretrained(model_name)
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self.pipeline = pipeline(
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task="mask-generation",
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model=self.model,
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image_processor=self.image_processor,
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device=device,
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)
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def run(self, image: np.ndarray) -> sv.Detections:
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"""
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Generate image segmentation marks.
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Parameters:
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image (np.ndarray): The image to be marked in BGR format.
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Returns:
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sv.Detections: An object containing the segmentation masks and their
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corresponding bounding box coordinates.
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"""
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image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB))
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outputs = self.pipeline(image, points_per_batch=64)
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masks = np.array(outputs["masks"])
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return masks_to_marks(masks=masks)
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@ -0,0 +1,13 @@
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# System prompt
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FLOW_SYSTEM_PROMPT = """
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You are an autonomous agent granted autonomy in a autonomous loop structure.
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Your role is to engage in multi-step conversations with your self or the user,
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generate long-form content like blogs, screenplays, or SOPs,
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and accomplish tasks bestowed by the user.
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You can have internal dialogues with yourself or can interact with the user
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to aid in these complex tasks. Your responses should be coherent, contextually relevant, and tailored to the task at hand.
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"""
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@ -0,0 +1,61 @@
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# Prompts
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DYNAMIC_STOP_PROMPT = """
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Now, when you 99% sure you have completed the task, you may follow the instructions below to escape the autonomous loop.
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When you have finished the task from the Human, output a special token: <DONE>
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This will enable you to leave the autonomous loop.
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"""
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# Make it able to handle multi input tools
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DYNAMICAL_TOOL_USAGE = """
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You have access to the following tools:
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Output a JSON object with the following structure to use the tools
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commands: {
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"tools": {
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tool1: "tool_name",
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"params": {
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"tool1": "inputs",
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"tool1": "inputs"
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}
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"tool2: "tool_name",
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"params": {
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"tool1": "inputs",
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"tool1": "inputs"
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}
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"tool3: "tool_name",
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"params": {
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"tool1": "inputs",
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"tool1": "inputs"
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}
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}
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}
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-------------TOOLS---------------------------
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{tools}
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"""
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SCENARIOS = """
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commands: {
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"tools": {
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tool1: "tool_name",
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"params": {
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"tool1": "inputs",
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"tool1": "inputs"
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}
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"tool2: "tool_name",
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"params": {
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"tool1": "inputs",
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"tool1": "inputs"
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}
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"tool3: "tool_name",
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"params": {
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"tool1": "inputs",
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"tool1": "inputs"
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}
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}
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}
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"""
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@ -1,97 +0,0 @@
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from swarms.models import OpenAIChat
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from swarms.structs.agent import Agent
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import concurrent.futures
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from typing import Callable, List, Dict, Any, Sequence
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class Task:
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def __init__(
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self,
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id: str,
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task: str,
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flows: Sequence[Agent],
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dependencies: List[str] = [],
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):
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self.id = id
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self.task = task
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self.flows = flows
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self.dependencies = dependencies
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self.results = []
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def execute(self, parent_results: Dict[str, Any]):
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args = [parent_results[dep] for dep in self.dependencies]
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for agent in self.flows:
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result = agent.run(self.task, *args)
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self.results.append(result)
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args = [
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result
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] # The output of one agent becomes the input to the next
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class Workflow:
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def __init__(self):
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self.tasks: Dict[str, Task] = {}
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self.executor = concurrent.futures.ThreadPoolExecutor()
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def add_task(self, task: Task):
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self.tasks[task.id] = task
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def run(self):
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completed_tasks = set()
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while len(completed_tasks) < len(self.tasks):
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futures = []
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for task in self.tasks.values():
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if task.id not in completed_tasks and all(
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dep in completed_tasks for dep in task.dependencies
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):
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future = self.executor.submit(
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task.execute,
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{
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dep: self.tasks[dep].results
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for dep in task.dependencies
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},
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)
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futures.append((future, task.id))
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for future, task_id in futures:
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future.result() # Wait for task completion
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completed_tasks.add(task_id)
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def get_results(self):
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return {task_id: task.results for task_id, task in self.tasks.items()}
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# create flows
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llm = OpenAIChat(openai_api_key="sk-")
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flow1 = Agent(llm, max_loops=1)
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flow2 = Agent(llm, max_loops=1)
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flow3 = Agent(llm, max_loops=1)
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flow4 = Agent(llm, max_loops=1)
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# Create tasks with their respective Agents and task strings
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task1 = Task("task1", "Generate a summary on Quantum field theory", [flow1])
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task2 = Task(
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"task2",
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"Elaborate on the summary of topic X",
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[flow2, flow3],
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dependencies=["task1"],
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)
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task3 = Task(
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"task3", "Generate conclusions for topic X", [flow4], dependencies=["task1"]
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)
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# Create a workflow and add tasks
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workflow = Workflow()
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workflow.add_task(task1)
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workflow.add_task(task2)
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workflow.add_task(task3)
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# Run the workflow
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workflow.run()
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# Get results
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results = workflow.get_results()
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print(results)
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@ -0,0 +1,49 @@
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from swarms.structs.agent import Agent
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from typing import List, Dict, Any, Sequence
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class Task:
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"""
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Task is a unit of work that can be executed by a set of agents.
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A task is defined by a task name and a set of agents that can execute the task.
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The task can also have a set of dependencies, which are the names of other tasks
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that must be executed before this task can be executed.
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Args:
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id (str): A unique identifier for the task
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task (str): The name of the task
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agents (Sequence[Agent]): A list of agents that can execute the task
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dependencies (List[str], optional): A list of task names that must be executed before this task can be executed. Defaults to [].
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Methods:
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execute(parent_results: Dict[str, Any]): Executes the task by passing the results of the parent tasks to the agents.
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"""
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def __init__(
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self,
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id: str,
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task: str,
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agents: Sequence[Agent],
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dependencies: List[str] = [],
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):
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self.id = id
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self.task = task
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self.agents = agents
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self.dependencies = dependencies
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self.results = []
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|
||||
def execute(self, parent_results: Dict[str, Any]):
|
||||
"""Executes the task by passing the results of the parent tasks to the agents.
|
||||
|
||||
Args:
|
||||
parent_results (Dict[str, Any]): _description_
|
||||
"""
|
||||
args = [parent_results[dep] for dep in self.dependencies]
|
||||
for agent in self.agents:
|
||||
result = agent.run(self.task, *args)
|
||||
self.results.append(result)
|
||||
args = [
|
||||
result
|
||||
] # The output of one agent becomes the input to the next
|
||||
@ -0,0 +1,106 @@
|
||||
import os
|
||||
from unittest.mock import Mock
|
||||
|
||||
import pytest
|
||||
from dotenv import load_dotenv
|
||||
|
||||
from swarms.models.gpt4_vision_api import GPT4VisionAPI
|
||||
from swarms.prompts.multi_modal_autonomous_instruction_prompt import (
|
||||
MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1,
|
||||
)
|
||||
from swarms.structs.agent import Agent
|
||||
from swarms.structs.task import Task
|
||||
|
||||
load_dotenv()
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def llm():
|
||||
return GPT4VisionAPI()
|
||||
|
||||
|
||||
def test_agent_run_task(llm):
|
||||
task = (
|
||||
"Analyze this image of an assembly line and identify any issues such as"
|
||||
" misaligned parts, defects, or deviations from the standard assembly"
|
||||
" process. IF there is anything unsafe in the image, explain why it is"
|
||||
" unsafe and how it could be improved."
|
||||
)
|
||||
img = "assembly_line.jpg"
|
||||
|
||||
agent = Agent(
|
||||
llm=llm,
|
||||
max_loops="auto",
|
||||
sop=MULTI_MODAL_AUTO_AGENT_SYSTEM_PROMPT_1,
|
||||
dashboard=True,
|
||||
)
|
||||
|
||||
result = agent.run(task=task, img=img)
|
||||
|
||||
# Add assertions here to verify the expected behavior of the agent's run method
|
||||
assert isinstance(result, dict)
|
||||
assert "response" in result
|
||||
assert "dashboard_data" in result
|
||||
# Add more assertions as needed
|
||||
|
||||
@pytest.fixture
|
||||
def task():
|
||||
agents = [Agent(llm=llm, id=f"Agent_{i}") for i in range(5)]
|
||||
return Task(id="Task_1", task="Task_Name", agents=agents, dependencies=[])
|
||||
|
||||
|
||||
# Basic tests
|
||||
|
||||
|
||||
def test_task_init(task):
|
||||
assert task.id == "Task_1"
|
||||
assert task.task == "Task_Name"
|
||||
assert isinstance(task.agents, list)
|
||||
assert len(task.agents) == 5
|
||||
assert isinstance(task.dependencies, list)
|
||||
|
||||
|
||||
def test_task_execute(task, mocker):
|
||||
mocker.patch.object(Agent, "run", side_effect=[1, 2, 3, 4, 5])
|
||||
parent_results = {}
|
||||
task.execute(parent_results)
|
||||
assert isinstance(task.results, list)
|
||||
assert len(task.results) == 5
|
||||
for result in task.results:
|
||||
assert isinstance(result, int)
|
||||
|
||||
|
||||
# Parameterized tests
|
||||
|
||||
|
||||
@pytest.mark.parametrize("num_agents", [1, 3, 5, 10])
|
||||
def test_task_num_agents(task, num_agents, mocker):
|
||||
task.agents = [Agent(id=f"Agent_{i}") for i in range(num_agents)]
|
||||
mocker.patch.object(Agent, "run", return_value=1)
|
||||
parent_results = {}
|
||||
task.execute(parent_results)
|
||||
assert len(task.results) == num_agents
|
||||
|
||||
|
||||
# Exception testing
|
||||
|
||||
|
||||
def test_task_execute_with_dependency_error(task, mocker):
|
||||
task.dependencies = ["NonExistentTask"]
|
||||
mocker.patch.object(Agent, "run", return_value=1)
|
||||
parent_results = {}
|
||||
with pytest.raises(KeyError):
|
||||
task.execute(parent_results)
|
||||
|
||||
|
||||
# Mocking and monkeypatching tests
|
||||
|
||||
|
||||
def test_task_execute_with_mocked_agents(task, mocker):
|
||||
mock_agents = [Mock(spec=Agent) for _ in range(5)]
|
||||
mocker.patch.object(task, "agents", mock_agents)
|
||||
for mock_agent in mock_agents:
|
||||
mock_agent.run.return_value = 1
|
||||
parent_results = {}
|
||||
task.execute(parent_results)
|
||||
assert len(task.results) == 5
|
||||
Loading…
Reference in new issue