style: format code with Autopep8, Black, isort, Prettier, StandardJS …

…and Yapf

This commit fixes the style issues introduced in d7c0184 according to the output
from Autopep8, Black, isort, Prettier, StandardJS and Yapf.

Details: None

tests/imageid/imageid.py

@@ -14,7 +14,8 @@
 detector = ObjectDetection()
 detector.setModelTypeAsRetinaNet()
 detector.setModelPath(
-    os.path.join(execution_path, "./testing/resnet50_coco_best_v2.0.1.h5"))
+    os.path.join(execution_path, "./testing/resnet50_coco_best_v2.0.1.h5")
+)
 detector.loadModel()
 custom_objects = detector.CustomObjects(
     person=True,

tests/imageid/test.py

@@ -4,6 +4,7 @@
 argument -- description
 Return: return_description
 """
+
 import os
 
 from imageai.Detection import ObjectDetection
@@ -13,7 +14,8 @@
 detector = ObjectDetection()
 detector.setModelTypeAsRetinaNet()
 detector.setModelPath(
-    os.path.join(execution_path, "./testing/resnet50_coco_best_v2.0.1.h5"))
+    os.path.join(execution_path, "./testing/resnet50_coco_best_v2.0.1.h5")
+)
 detector.loadModel()
 custom_objects = detector.CustomObjects(
     person=True,

tests/open_images/0.py

@@ -1,8 +1,8 @@
 info = data_other.iloc[data_idx]  # getting the info of the index
 img = cv2.imread(f'./Img/{info["Path"]}')  # reading the img
-height, width = cv2.imread(
-    "./Img/" +
-    info["Path"]).shape[:2]  # getting the height and width of the image
+height, width = cv2.imread("./Img/" + info["Path"]).shape[
+    :2
+]  # getting the height and width of the image
 xmin, ymin, xmax, ymax = (
     info["XMin"],
     info["YMin"],
@@ -19,6 +19,5 @@
 w = xmax - xmin
 h = ymax - ymin
 x, y, w, h = round(x), round(y), round(w), round(h)
-roi = img[y:y + h, x:x + w]  # crop the image
-cv2.rectangle(img, (x, y), (x + w, y + h), (200, 0, 0),
-              10)  # draw box around the bbox
+roi = img[y : y + h, x : x + w]  # crop the image
+cv2.rectangle(img, (x, y), (x + w, y + h), (200, 0, 0), 10)  # draw box around the bbox

tests/open_images/find_card_v1.py

@@ -21,7 +21,8 @@
     sat = hsv[:, :, 1]  # Convert the hsv Image to GrayScale
     cv2.imwrite("sat.png", sat)  # Saving the Sat
     thresh = cv2.threshold(sat, 15, 255, cv2.THRESH_BINARY)[
-        1]  # The Outline of the Sat which is the GrayScale Img
+        1
+    ]  # The Outline of the Sat which is the GrayScale Img
     cv2.imwrite("thresh.png", thresh)  # Saving the thresh
     thresh = 255 - thresh  # Converts Black to White and White to Black
     cv2.imwrite("thresh-2.png", thresh)  # Saving the thresh
@@ -42,19 +43,20 @@
         im, cv2.COLOR_BGR2GRAY
     )  # Conver the black and white image to a image like https://i.stack.imgur.com/UPOZC.png
     cv2.imwrite("gray.png", gray)  # Saving the gray image
-    contours, _ = cv2.findContours(
-        gray, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[-2:]  # Finding the boxes
+    contours, _ = cv2.findContours(gray, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[
+        -2:
+    ]  # Finding the boxes
 
     for cnt in contours:
         x, y, w, h = cv2.boundingRect(cnt)  # Convert cnt to x,y,w,h
         if (
-                w > 250 and h > 250
+            w > 250 and h > 250
         ):  # Checking if the h and w of the image is higher than 175 so this will only get the card
             idx += 1
             cv2.rectangle(img, (x, y), (x + w, y + h), (200, 0, 0))
             img = cv2.imread(f"./Imgs/{file}")  # get the original image
             img = cv2.resize(img, (2000, 2000))
-            crop_img = img[y:y + h, x:x + w]  # Cropping
+            crop_img = img[y : y + h, x : x + w]  # Cropping
             if f"./Imgs/{file}" in list(data):
                 data[f"{file}"]["X"].append(x)
                 data[f"{file}"]["Y"].append(y)
@@ -80,5 +82,5 @@
         f"./Preds/{file}.jpeg",
         cv2.rectangle(img, (X, Y), (X + W, Y + H), (200, 0, 0)),
     )  # Saving the corped image
-    crop_img = img[Y:Y + H, X:X + W]  # Cropping
+    crop_img = img[Y : Y + H, X : X + W]  # Cropping
     cv2.imwrite(f"./Imgs/{key}", crop_img)  # Saving the corped image

tests/pytorch-lightning/Learning-PyTorch-Lightning-main/0.py

@@ -46,25 +46,22 @@ def training_step(self, batch, batch_idx):
     @staticmethod
     def train_dataloader():
         train_dataset = torchvision.datasets.MNIST(
-            root="./data",
-            train=True,
-            transform=transforms.ToTensor(),
-            download=True)
-        train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
-                                                   batch_size=batch_size,
-                                                   num_workers=4,
-                                                   shuffle=False)
+            root="./data", train=True, transform=transforms.ToTensor(), download=True
+        )
+        train_loader = torch.utils.data.DataLoader(
+            dataset=train_dataset, batch_size=batch_size, num_workers=4, shuffle=False
+        )
         return train_loader
 
     @staticmethod
     def val_dataloader():
         test_dataset = torchvision.datasets.MNIST(
-            root="./data", train=False, transform=transforms.ToTensor())
+            root="./data", train=False, transform=transforms.ToTensor()
+        )
 
-        test_loader = torch.utils.data.DataLoader(dataset=test_dataset,
-                                                  batch_size=batch_size,
-                                                  num_workers=4,
-                                                  shuffle=False)
+        test_loader = torch.utils.data.DataLoader(
+            dataset=test_dataset, batch_size=batch_size, num_workers=4, shuffle=False
+        )
         return test_loader
 
     def validation_step(self, batch, batch_idx):

tests/tflite/test.py

@@ -4,6 +4,7 @@
 argument -- description
 Return: return_description
 """
+
 import datetime
 import os
 

tests/tflite/tflite.py

@@ -8,8 +8,7 @@
 from tflite_model_maker import model_spec, object_detector
 from tflite_model_maker.config import ExportFormat, QuantizationConfig
 
-print("Num GPUs Available: ",
-      len(tf.config.experimental.list_physical_devices("GPU")))
+print("Num GPUs Available: ", len(tf.config.experimental.list_physical_devices("GPU")))
 
 
 class TFLite:
@@ -29,8 +28,7 @@ def __init__(
         train_whole_model: bool = True,
         export_dir: str = ".",
         model_path: str = "model.tflite",
-        data_loading_csv:
-        str = "gs://cloud-ml-data/img/openimage/csv/salads_ml_use.csv",
+        data_loading_csv: str = "gs://cloud-ml-data/img/openimage/csv/salads_ml_use.csv",
     ) -> None:
         """sumary_line
         Keyword arguments:
@@ -43,8 +41,7 @@ def __init__(
         self.train_whole_model = train_whole_model
         self.export_dir = export_dir
         self.data_loading_csv = data_loading_csv
-        self.log_dir = "logs/fit/" + datetime.datetime.now().strftime(
-            "%Y%m%d-%H%M%S")
+        self.log_dir = "logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
         self.epochs = epochs
         self.threshold = 0.125
         self.model_path = model_path
@@ -136,21 +133,27 @@ def create_test_image(self, image_path):
         """
         with tf.device("/GPU:0"):
             print("Creating test images")
-            _, input_height, input_width, _ = self.interpreter.get_input_details(
-            )[0]["shape"]
+            _, input_height, input_width, _ = self.interpreter.get_input_details()[0][
+                "shape"
+            ]
             # Read image in tf encoded format
             img = tf.io.read_file(image_path)
             img = tf.io.decode_image(
-                img, channels=3)  # Decode the image (Load the image)
+                img, channels=3
+            )  # Decode the image (Load the image)
             img = tf.image.convert_image_dtype(
-                img, tf.uint8)  # Convert to Data Type Unit8
+                img, tf.uint8
+            )  # Convert to Data Type Unit8
             self.original_image = img
             self.resized_img = tf.image.resize(
-                img, (input_height, input_width))  # Resize Image
+                img, (input_height, input_width)
+            )  # Resize Image
             self.resized_img = self.resized_img[
-                tf.newaxis, :]  # Add 1 dimension to the image
+                tf.newaxis, :
+            ]  # Add 1 dimension to the image
             self.resized_img = tf.cast(
-                self.resized_img, dtype=tf.uint8)  # Convert to Data Type Unit8
+                self.resized_img, dtype=tf.uint8
+            )  # Convert to Data Type Unit8
 
     def predict_test_image(self):
         """sumary_line

tests/torch_object_detection/data_loader.py

@@ -68,13 +68,13 @@ def __getitem__(self, idx):
         # convert everything into a torch.Tensor
         boxes = torch.as_tensor(boxes, dtype=torch.float32)
         # there is only one class
-        labels = torch.ones((num_objs, ), dtype=torch.int64)
+        labels = torch.ones((num_objs,), dtype=torch.int64)
         masks = torch.as_tensor(masks, dtype=torch.uint8)
 
         image_id = torch.tensor([idx])
         area = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
         # suppose all instances are not crowd
-        iscrowd = torch.zeros((num_objs, ), dtype=torch.int64)
+        iscrowd = torch.zeros((num_objs,), dtype=torch.int64)
 
         target = {
             "boxes": boxes,
@@ -95,7 +95,6 @@ def __len__(self):
         return len(self.imgs)
 
 
-dl = torch.utils.data.DataLoader(PennFudanDataset(),
-                                 batch_size=32,
-                                 shuffle=True,
-                                 num_workers=2)
+dl = torch.utils.data.DataLoader(
+    PennFudanDataset(), batch_size=32, shuffle=True, num_workers=2
+)