BCCD Preprocess

BCC-preprocess2.py

img_width = 640
img_height = 480

def width(df):
  return int(df.xmax - df.xmin)
def height(df):
  return int(df.ymax - df.ymin)
def x_center(df):
  return int(df.xmin + (df.width/2))
def y_center(df):
  return int(df.ymin + (df.height/2))
def w_norm(df):
  return df/img_width
def h_norm(df):
  return df/img_height

df = pd.read_csv('/content/blood_cell_detection.csv')

le = preprocessing.LabelEncoder()
le.fit(df['cell_type'])
print(le.classes_)
labels = le.transform(df['cell_type'])
df['labels'] = labels

df['width'] = df.apply(width, axis=1)
df['height'] = df.apply(height, axis=1)

df['x_center'] = df.apply(x_center, axis=1)
df['y_center'] = df.apply(y_center, axis=1)

df['x_center_norm'] = df['x_center'].apply(w_norm)
df['width_norm'] = df['width'].apply(w_norm)

df['y_center_norm'] = df['y_center'].apply(h_norm)
df['height_norm'] = df['height'].apply(h_norm)

df.head(30)