Fix filename typo

csv_to_numpy.py

@@ -0,0 +1,87 @@
+from constants import CASC_PATH, SIZE_FACE, EMOTIONS, SAVE_DATASET_IMAGES_FILENAME, SAVE_DATASET_LABELS_FILENAME, SAVE_DIRECTORY, DATASET_CSV_FILENAME
+import cv2
+import pandas as pd
+import numpy as np
+from PIL import Image
+from os.path import join
+
+
+cascade_classifier = cv2.CascadeClassifier(CASC_PATH)
+
+
+def format_image(image):
+    if len(image.shape) > 2 and image.shape[2] == 3:
+        image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    else:
+        image = cv2.imdecode(image, cv2.CV_LOAD_IMAGE_GRAYSCALE)
+    gray_border = np.zeros((150, 150), np.uint8)
+    gray_border[:, :] = 200
+    gray_border[
+        int((150 / 2) - (SIZE_FACE / 2)): int((150 / 2) + (SIZE_FACE / 2)),
+        int((150 / 2) - (SIZE_FACE / 2)): int((150 / 2) + (SIZE_FACE / 2))
+    ] = image
+    image = gray_border
+    faces = cascade_classifier.detectMultiScale(
+        image,
+        scaleFactor=1.3,
+        minNeighbors=5
+    )
+    #  None is we don't found an image
+    if not len(faces) > 0:
+        return None
+    max_area_face = faces[0]
+    for face in faces:
+        if face[2] * face[3] > max_area_face[2] * max_area_face[3]:
+            max_area_face = face
+    # Chop image to face
+    face = max_area_face
+    image = image[face[1]:(face[1] + face[2]), face[0]:(face[0] + face[3])]
+    # Resize image to network size
+
+    try:
+        image = cv2.resize(image, (SIZE_FACE, SIZE_FACE),
+                           interpolation=cv2.INTER_CUBIC) / 255.
+    except Exception:
+        print("[+] Problem during resize")
+        return None
+    return image
+
+
+def emotion_to_vec(x):
+    d = np.zeros(len(EMOTIONS))
+    d[x] = 1.0
+    return d
+
+
+def flip_image(image):
+    return cv2.flip(image, 1)
+
+
+def data_to_image(data):
+    data_image = np.fromstring(
+        str(data), dtype=np.uint8, sep=' ').reshape((SIZE_FACE, SIZE_FACE))
+    data_image = Image.fromarray(data_image).convert('RGB')
+    data_image = np.array(data_image)[:, :, ::-1].copy()
+    data_image = format_image(data_image)
+    return data_image
+
+
+data = pd.read_csv(join(SAVE_DIRECTORY, DATASET_CSV_FILENAME))
+
+labels = []
+images = []
+index = 1
+total = data.shape[0]
+for index, row in data.iterrows():
+    emotion = emotion_to_vec(row['emotion'])
+    image = data_to_image(row['pixels'])
+    if image is not None:
+        labels.append(emotion)
+        images.append(image)
+        # images.append(flip_image(image))
+    index += 1
+    print("Progress: {}/{} {:.2f}%".format(index, total, index * 100.0 / total))
+
+print("Total: " + str(len(images)))
+np.save(join(SAVE_DIRECTORY, SAVE_DATASET_IMAGES_FILENAME), images)
+np.save(join(SAVE_DIRECTORY, SAVE_DATASET_LABELS_FILENAME), labels)