👕Linting

pose_classification_kit/__init__.py

@@ -1 +1 @@
-__version__ = "1.1.4"
+__version__ = "1.1.5"

pose_classification_kit/datasets/__init__.py

@@ -7,25 +7,21 @@
 from .data_augmentation import dataAugmentation
 
 
-def importBodyCSVDataset(testSplit):
+def importBodyCSVDataset(testSplit: float, local_import: bool):
     """Import body dataset as numpy arrays from GitHub if available, or local dataset otherwise.
 
     Args:
         testSplit (float, optional): Percentage of the dataset reserved for testing. Defaults to 0.15. Must be between 0.0 and 1.0.
     """
+    assert 0.0 <= testSplit <= 1.0
+
     datasetPath = DATASETS_PATH / "BodyPose_Dataset.csv"
     datasetURL = "https://raw.githubusercontent.com/ArthurFDLR/pose-classification-kit/master/pose_classification_kit/datasets/BodyPose_Dataset.csv"
 
-    assert 0.0 <= testSplit <= 1.0
-
-    # Try to fetch the most recent dataset, load local file otherwise.
-    try:
-        dataset_df = pd.read_csv(datasetURL)
-        print("Dataset loaded from", datasetURL)
-    except:
-        assert datasetPath.is_file(), "No local dataset found."
+    if local_import:
         dataset_df = pd.read_csv(datasetPath)
-        print("Dataset loaded from", str(datasetPath))
+    else:
+        dataset_df = pd.read_csv(datasetURL)
 
     bodyLabels_df = dataset_df.groupby("label")
     labels = list(dataset_df.label.unique())
@@ -63,7 +59,10 @@ def importBodyCSVDataset(testSplit):
 
 
 def bodyDataset(
-    testSplit: float = 0.15, shuffle: bool = True, bodyModel: BodyModel = BODY25
+    testSplit: float = 0.15,
+    shuffle: bool = True,
+    bodyModel: BodyModel = BODY25,
+    local_import: bool = False,
 ):
     """Return the dataset of body keypoints (see pose_classification_kit/datasets/BodyPose_Dataset.csv)
     as numpy arrays.
@@ -72,6 +71,7 @@ def bodyDataset(
         testSplit (float, optional): Percentage of the dataset reserved for testing. Defaults to 0.15. Must be between 0.0 and 1.0.
         shuffle (bool, optional): Shuffle the whole dataset. Defaults to True.
         bodyModel (BodyModel, optional): Select the keypoint format of the dataset. BODY25 or BODY18. Defaults to BODY25.
+        local_import (bool, optional): Choose to use local dataset or fetch online dataset (global repository). Default False.
 
     Returns:
         dict: {
@@ -85,7 +85,9 @@ def bodyDataset(
     }
     """
 
-    x_train, x_test, y_train, y_test, labels = importBodyCSVDataset(testSplit)
+    x_train, x_test, y_train, y_test, labels = importBodyCSVDataset(
+        testSplit, local_import
+    )
 
     # Shuffle in unison
     if shuffle:
@@ -121,14 +123,20 @@ def bodyDataset(
     }
 
 
-def handDataset(testSplit: float = 0.15, shuffle: bool = True, handID: int = 0):
+def handDataset(
+    testSplit: float = 0.15,
+    shuffle: bool = True,
+    handID: int = 0,
+    local_import: bool = False,
+):
     """Return the dataset of hand keypoints (see pose_classification_kit/datasets/HandPose_Dataset.csv)
     as numpy arrays.
 
     Args:
         testSplit (float, optional): Percent of the dataset reserved for testing. Defaults to 0.15. Must be between 0.0 and 1.0.
         shuffle (bool, optional): Shuffle the whole dataset. Defaults to True.
         handID (int, optional): Select hand side - 0:left, 1:right. Default to 0.
+        local_import (bool, optional): Choose to use local dataset or fetch online dataset (global repository). Default False.
 
     Returns:
         dict: {
@@ -141,19 +149,15 @@ def handDataset(testSplit: float = 0.15, shuffle: bool = True, handID: int = 0):
         'labels': list of labels
     }
     """
+    assert 0.0 <= testSplit <= 1.0
+
     datasetPath = DATASETS_PATH / "HandPose_Dataset.csv"
     datasetURL = "https://raw.githubusercontent.com/ArthurFDLR/pose-classification-kit/master/pose_classification_kit/datasets/HandPose_Dataset.csv"
 
-    assert 0.0 <= testSplit <= 1.0
-
-    # Try to fetch the most recent dataset, load local file otherwise.
-    try:
-        dataset_df = pd.read_csv(datasetURL)
-        print("Dataset loaded from", datasetURL)
-    except:
-        assert datasetPath.is_file(), "No local dataset found."
+    if local_import:
         dataset_df = pd.read_csv(datasetPath)
-        print("Dataset loaded from", str(datasetPath))
+    else:
+        dataset_df = pd.read_csv(datasetURL)
 
     hand_label = "right" if handID else "left"
     handLabels_df = {

pose_classification_kit/datasets/body_models.py

@@ -93,27 +93,27 @@ def __init__(self, mapping, pairs) -> None:
         "neck_y",
     ],
     pairs=[
-        [(30,31), (26,27)],
-        [(26,27), (22,23)],
-        [(32,33), (28,29)],
-        [(28,29), (24,25)],
-        [(22,23), (24,25)],
-        [(10,11), (14,15)],
-        [(12,13), (16,17)],
-        [(14,15), (18,19)],
-        [(16,17), (20,21)],
-        [(2,3), (4,5)],
-        [(0,1), (2,3)],
-        [(0,1), (4,5)],
-        [(2,3), (6,7)],
-        [(4,5), (8,9)],
-        [(6,7), (10,11)],
-        [(8,9), (12,13)],
-        [(34,35), (0,1)],
-        [(34,35), (10,11)],
-        [(34,35), (12,13)],
-        [(34,35), (22,23)],
-        [(34,35), (24,25)],
+        [(30, 31), (26, 27)],
+        [(26, 27), (22, 23)],
+        [(32, 33), (28, 29)],
+        [(28, 29), (24, 25)],
+        [(22, 23), (24, 25)],
+        [(10, 11), (14, 15)],
+        [(12, 13), (16, 17)],
+        [(14, 15), (18, 19)],
+        [(16, 17), (20, 21)],
+        [(2, 3), (4, 5)],
+        [(0, 1), (2, 3)],
+        [(0, 1), (4, 5)],
+        [(2, 3), (6, 7)],
+        [(4, 5), (8, 9)],
+        [(6, 7), (10, 11)],
+        [(8, 9), (12, 13)],
+        [(34, 35), (0, 1)],
+        [(34, 35), (10, 11)],
+        [(34, 35), (12, 13)],
+        [(34, 35), (22, 23)],
+        [(34, 35), (24, 25)],
     ],
 )
 
@@ -174,7 +174,7 @@ def __init__(self, mapping, pairs) -> None:
         [11, 24],
     ],
 )
-''' #BODY25 annotated
+""" #BODY25 annotated
     pairs_annotated={
         "Torso":[1, 8], 
         "Shoulder (right)":[1, 2],
@@ -201,7 +201,7 @@ def __init__(self, mapping, pairs) -> None:
         "Toe (right)":[22, 23],
         "Heel (right)":[11, 24],
     }
-'''
+"""
 
 BODY25_FLAT = BodyModel(
     mapping=[
@@ -257,32 +257,32 @@ def __init__(self, mapping, pairs) -> None:
         "right_heel_y",
     ],
     pairs=[
-        [(2,3), (16,17)],
-        [(2,3), (4,5)],
-        [(2,3), (10,11)],
-        [(4,5), (6,7)],
-        [(6,7), (8,9)],
-        [(10,11), (12,13)],
-        [(12,13), (14,15)],
-        [(16,17), (18,19)],
-        [(18,19), (20,21)],
-        [(20,21), (22,23)],
-        [(16,17), (24,25)],
-        [(24,25), (26,27)],
-        [(26,27), (28,29)],
-        [(2,3), (0,1)],
-        [(0,1), (30,31)],
-        [(30,31), (34,35)],
-        [(0,1), (32,33)],
-        [(32,33), (36,37)],
-        [(4,5), (34,35)],
-        [(10,11), (36,37)],
-        [(28,29), (38,39)],
-        [(38,39), (40,41)],
-        [(28,29), (42,43)],
-        [(22,23), (44,45)],
-        [(44,45), (46,47)],
-        [(22,23), (48,49)],
+        [(2, 3), (16, 17)],
+        [(2, 3), (4, 5)],
+        [(2, 3), (10, 11)],
+        [(4, 5), (6, 7)],
+        [(6, 7), (8, 9)],
+        [(10, 11), (12, 13)],
+        [(12, 13), (14, 15)],
+        [(16, 17), (18, 19)],
+        [(18, 19), (20, 21)],
+        [(20, 21), (22, 23)],
+        [(16, 17), (24, 25)],
+        [(24, 25), (26, 27)],
+        [(26, 27), (28, 29)],
+        [(2, 3), (0, 1)],
+        [(0, 1), (30, 31)],
+        [(30, 31), (34, 35)],
+        [(0, 1), (32, 33)],
+        [(32, 33), (36, 37)],
+        [(4, 5), (34, 35)],
+        [(10, 11), (36, 37)],
+        [(28, 29), (38, 39)],
+        [(38, 39), (40, 41)],
+        [(28, 29), (42, 43)],
+        [(22, 23), (44, 45)],
+        [(44, 45), (46, 47)],
+        [(22, 23), (48, 49)],
     ],
 )
 
@@ -291,6 +291,7 @@ def __init__(self, mapping, pairs) -> None:
 BODY25flat_to_BODY18flat_indices = [0, 1, 32, 33, 30, 31, 36, 37, 34, 35, 10, 11, 4, 5, 12, 13, 6, 7, 14, 15, 8, 9, 24, 25, 18, 19, 26, 27, 20, 21, 28, 29, 22, 23, 2, 3]
 # fmt: on
 
+
 def BODY25_to_BODY18(body25_keypoints: np.ndarray):
     assert body25_keypoints.shape == 25
     return body25_keypoints[BODY25_to_BODY18_indices]

pose_classification_kit/datasets/data_augmentation.py

@@ -116,12 +116,12 @@ def dataAugmentation(
             # Remove the points
             if type(remove_rand_keypoints_nbr) != type(None):
                 if i in list_random_keypoints:
-                    keypoint_x = 0.
-                    keypoint_y = 0.
+                    keypoint_x = 0.0
+                    keypoint_y = 0.0
             if type(remove_specific_keypoints) != type(None):
                 if i in remove_specific_keypoints:
-                    keypoint_x = 0.
-                    keypoint_y = 0.
+                    keypoint_x = 0.0
+                    keypoint_y = 0.0
             # Add additionnal augmentation features
             entry.append([keypoint_x, keypoint_y])
 
@@ -137,4 +137,4 @@ def dataAugmentation(
     new_x = np.array(new_x)
     new_y = np.array(new_y)
 
-    return (new_x,new_y)
+    return (new_x, new_y)

pose_classification_kit/src/keypoints_analysis/body_analysis.py

@@ -6,7 +6,13 @@
     from ..imports.openpose import op
 from .dynamic_bar_graph_widget import BarGraphWidget
 from .classifier_selection_widget import ClassifierSelectionWidget
-from ...datasets.body_models import BODY18, BODY18_FLAT, BODY25, BODY25_FLAT, BODY25_to_BODY18_indices
+from ...datasets.body_models import (
+    BODY18,
+    BODY18_FLAT,
+    BODY25,
+    BODY25_FLAT,
+    BODY25_to_BODY18_indices,
+)
 
 import numpy as np
 from matplotlib.backends.backend_qt5agg import FigureCanvas
@@ -162,16 +168,18 @@ def updatePredictedClass(self, keypoints: np.ndarray):
         title = ""
         if type(keypoints) != type(None):
             if self.modelClassifier is not None:
-                
+
                 if self.currentBodyModel == BODY25:
                     inputData = keypoints[:2].T
                 elif self.currentBodyModel == BODY25_FLAT:
                     inputData = np.concatenate(keypoints[:2].T, axis=0)
                 elif self.currentBodyModel == BODY18:
-                    inputData = keypoints.T[BODY25_to_BODY18_indices][:,:2]
+                    inputData = keypoints.T[BODY25_to_BODY18_indices][:, :2]
                 elif self.currentBodyModel == BODY18_FLAT:
-                    inputData = np.concatenate(keypoints.T[BODY25_to_BODY18_indices][:,:2], axis=0)
-
+                    inputData = np.concatenate(
+                        keypoints.T[BODY25_to_BODY18_indices][:, :2], axis=0
+                    )
+
                 prediction = self.modelClassifier.predict(np.array([inputData]))[0]
                 self.currentPrediction = self.classOutputs[np.argmax(prediction)]
                 title = self.currentPrediction
@@ -187,10 +195,14 @@ def newModelLoaded(self, urlModel: str, modelInfo: dict, bodyID: int):
                 if bodyID == 2:  # Check if classifier for body poses (not hands)
                     model = tf.keras.models.load_model(urlModel)
                     nbrClass = model.layers[-1].output_shape[1]
-                    if modelInfo and modelInfo.get('labels') and len(modelInfo.get('labels')) == nbrClass:
-                        classOutputs = modelInfo.get('labels')
+                    if (
+                        modelInfo
+                        and modelInfo.get("labels")
+                        and len(modelInfo.get("labels")) == nbrClass
+                    ):
+                        classOutputs = modelInfo.get("labels")
                     else:
-                        classOutputs = [str(i) for i in range(1,nbrClass+1)]
+                        classOutputs = [str(i) for i in range(1, nbrClass + 1)]
                     self.setClassifierModel(model, classOutputs)
 
     def getCurrentPrediction(self) -> str: