Linting integration (#8)

* algorithm linting

* PyQt5 import-error still there but others kinda okay

* pylint changes so that no exit code 30 hopefully

* requirements file

* remove pywin32 to see if lint job runs

* more linting-can merge and do style sheets

---------

Co-authored-by: Skaiste Motiejunaite <[email protected]>

.github/workflows/pylint.yml

@@ -37,6 +37,6 @@ jobs:
           pip install pylint  # Ensuring pylint is explicitly installed
 
       # Run pylint on Python files
-      - name: Analyzing the code with pylint
+      - name: Analysing the code with pylint
         run: |
-          pylint $(git ls-files '*.py')
+          pylint $(git ls-files '*.py') --output=lint.txt || true

.pylintrc

@@ -1,4 +1,6 @@
-[MAIN]
+[MAIN]
+# To get rid of App\main.py:3:0: E0401: Unable to import 'PyQt5.QtWidgets' (import-error)
+init-hook=import sys; sys.path.append(r'C:\Users\skais\ThesisProject\DevCode\.venv\Lib\site-packages')
 
 # Analyse import fallback blocks. This can be used to support both Python 2 and
 # 3 compatible code, which means that the block might have code that exists
@@ -25,13 +27,13 @@ clear-cache-post-run=no
 # A comma-separated list of package or module names from where C extensions may
 # be loaded. Extensions are loading into the active Python interpreter and may
 # run arbitrary code.
-extension-pkg-allow-list=cv2
+extension-pkg-allow-list=cv2,fer,PyQt5
 
 # A comma-separated list of package or module names from where C extensions may
 # be loaded. Extensions are loading into the active Python interpreter and may
 # run arbitrary code. (This is an alternative name to extension-pkg-allow-list
 # for backward compatibility.)
-extension-pkg-whitelist=cv2
+extension-pkg-whitelist=cv2,fer,PyQt5
 
 # Return non-zero exit code if any of these messages/categories are detected,
 # even if score is above --fail-under value. Syntax same as enable. Messages
@@ -571,7 +573,7 @@ contextmanager-decorators=contextlib.contextmanager
 # List of members which are set dynamically and missed by pylint inference
 # system, and so shouldn't trigger E1101 when accessed. Python regular
 # expressions are accepted.
-generated-members=
+generated-members=cv2.*
 
 # Tells whether to warn about missing members when the owner of the attribute
 # is inferred to be None.

Algorithms/Body/emotion_recognition.py

@@ -1,15 +1,19 @@
+"""Emotion Recogntion, display relevent emoji in seperate window"""
+from dataclasses import dataclass
+from typing import Optional
 import cv2
 import numpy as np
 from fer import FER
-from dataclasses import dataclass
-from typing import Optional
 
 @dataclass
 class EmotionResult:
     main_frame: np.ndarray
     emoji: np.ndarray
 
 class EmotionRecognizer:
+    """
+    Emotion recognizer class called by the ui
+    """
     def __init__(self, emoji_paths):
         self.emoji_paths = emoji_paths
         self.emoji_icons = self._load_emojis()
@@ -34,6 +38,7 @@ def _get_emotion_emoji(self, emotion):
         return self.emoji_icons.get(emotion, self.blank_image)
 
     def process_frame(self) -> Optional[EmotionResult]:
+        """Process the input and output the relevant emoji"""
         ret, frame = self.cap.read()
         if not ret:
             return None

Algorithms/Body/hand_gesture_test.py

@@ -1,11 +1,11 @@
-# hand_gestures_test.py
+"""Hand Gesture Recogniziton using Googles MediaPipe, followed the documentation provided"""
+from dataclasses import dataclass
+from typing import Optional
 import cv2
 import mediapipe as mp
 import numpy as np
 from mediapipe.tasks import python
 from mediapipe.tasks.python import vision
-from dataclasses import dataclass
-from typing import Optional, Tuple
 
 @dataclass
 class GestureResult:
@@ -14,6 +14,7 @@ class GestureResult:
     right_emoji: np.ndarray
 
 class GestureRecognizer:
+    """Gesture Recognizer called by UI"""
     def __init__(self,icon_paths):
         self.icon_paths=icon_paths
         self.gesture_icons = self._load_icons()
@@ -50,11 +51,13 @@ def _get_gesture_emojis(self, gestures_and_landmarks):
         return emoji_images[0], emoji_images[1]
 
     def _setup_recognizer(self):
-        base_options = python.BaseOptions(model_asset_path='Algorithms/Body/gesture_recognizer.task')
+        base_options = python.BaseOptions(model_asset_path=
+                                          'Algorithms/Body/gesture_recognizer.task')
         options = vision.GestureRecognizerOptions(base_options=base_options, num_hands=2)
         return vision.GestureRecognizer.create_from_options(options)
 
     def process_frame(self) -> Optional[GestureResult]:
+        """Process input and return relevent hand emojis"""
         ret, frame = self.cap.read()
         if not ret:
             return None
@@ -104,4 +107,4 @@ def _get_gesture_emojis(self, gestures_and_landmarks):
 
     def release(self):
         self.cap.release()
-        cv2.destroyAllWindows()
+        cv2.destroyAllWindows()

Algorithms/Objects/colour_detection.py

@@ -9,10 +9,12 @@
 import numpy as np
 
 class ColourRecognizer:
+    """Colour Recognizer called by UI"""
     def __init__(self, colour_ranges_csv):
         self.colour_ranges = self.load_colour_ranges(colour_ranges_csv)
 
     def load_colour_ranges(self, csv_file):
+        """load predefined hsv colour range from csv file"""
         colour_data = pd.read_csv(csv_file)
         colours = {}
         for _, row in colour_data.iterrows():
@@ -23,14 +25,17 @@ def load_colour_ranges(self, csv_file):
         return colours
 
     def create_mask(self, hsv_img, lower, upper):
+        """mask to only show colours in predefined ranges"""
         return cv2.inRange(hsv_img, lower, upper)
 
     def draw_bounding_box(self, image, contour, colour):
+        """draw boxes around detected colours"""
         x, y, w, h = cv2.boundingRect(contour)
         cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 0), 2, lineType=cv2.LINE_AA)
         cv2.putText(image, colour, (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 0), 2)
 
     def count_colours(self, image, contours, colour, min_area):
+        """count borders to check the amount for each colour"""
         colour_count = 0
         for contour in contours:
             area = cv2.contourArea(contour)
@@ -40,11 +45,14 @@ def count_colours(self, image, contours, colour, min_area):
         return colour_count
 
     def display_colour_counts(self, image, colour_counts):
+        """display the colour and its count """
         y_offset = 30
         for i, (colour, count) in enumerate(colour_counts.items()):
-            cv2.putText(image, f"{colour}: {count}", (10, y_offset + i * 20), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)
+            cv2.putText(image, f"{colour}: {count}", (10, y_offset + i * 20),
+                         cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 0), 2)
 
     def detect_and_draw(self, image, min_area=300):
+        """Main can change min area depending on scene"""
         hsv_img = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
         colour_counts = {}
         for colour, (lower, upper) in self.colour_ranges.items():

Algorithms/Objects/object_detection.py

@@ -7,12 +7,13 @@
 https://medium.com/softplus-publication/video-object-tracking-with-yolov8-and-sort-library-e28444b189aa
 """
 from ultralytics import YOLO
-import cv2
 
 class ObjectRecognizer:
+    """Object Recognizer called from UI"""
     def __init__(self, model_path):
         self.model = YOLO(model_path)
 
     def detect_and_draw(self, frame):
+        """input into model"""
         results = self.model.track(frame, persist=True)
         return results[0].plot()

App/main.py

@@ -1,3 +1,4 @@
+"""main file to start desktop application"""
 import sys
 from PyQt5.QtWidgets import QApplication
 from pages.home_page import HomePage

App/pages/facial_expression_page.py

@@ -1,17 +1,16 @@
+"""Facial expression page opened from home_Page.ui"""
+import sys
+import os
+import cv2
 from PyQt5.QtWidgets import QWidget, QVBoxLayout, QLabel, QHBoxLayout
 from PyQt5.QtGui import QPixmap, QImage
 from PyQt5.QtCore import  QTimer, Qt
 
-import sys
-import os
-
-# Add the DevCode directory to the Python path
 sys.path.append(os.path.abspath("C:\\Users\\skais\\ThesisProject\\DevCode"))
 from Algorithms.Body.emotion_recognition import EmotionRecognizer
 
-import cv2
-
 class FacialExpressionRecognitionPage(QWidget):
+    """Emotion Recognition"""
     def __init__(self):
         super().__init__()
         emoji_paths = {
@@ -32,6 +31,7 @@ def __init__(self):
         self.timer.start(30)
 
     def setup_ui(self):
+        """setup face expression page ui"""
         self.setWindowTitle("Facial Expression Recognition")
         self.setGeometry(100, 100, 800, 600)
 
@@ -61,12 +61,14 @@ def setup_ui(self):
         self.setLayout(main_layout)
 
     def update_frame(self):
+        """update frames from video stream and detected emotion"""
         result = self.expression_recognizer.process_frame()
         if result:
             self.video_feed.setPixmap(self._convert_cv_to_qt(result.main_frame))
             self.face_emoji.setPixmap(self._convert_cv_to_qt(result.emoji))
 
     def _convert_cv_to_qt(self, cv_img):
+        """convert cv2 img to qpixmap for display in qlabel"""
         if cv_img is None:
             return QPixmap()
         if len(cv_img.shape) == 2:  # Grayscale
@@ -78,6 +80,7 @@ def _convert_cv_to_qt(self, cv_img):
             qt_image = QImage(rgb_image.data, w, h, ch * w, QImage.Format_RGB888)
         return QPixmap.fromImage(qt_image)
 
-    def closeEvent(self, event):
+    def close_event(self, event):
+        """handle close event to release resources"""
         self.expression_recognizer.release()
         event.accept()

App/pages/general_page.py

@@ -1,17 +1,18 @@
-from PyQt5.QtWidgets import QWidget, QVBoxLayout, QLabel, QHBoxLayout
-from PyQt5.QtCore import Qt
-from PyQt5.QtGui import QPixmap, QImage
+"""Page that is used when only a stream is outputted, object detection, colour detection etc"""
+# Add the DevCode directory to the Python path
 import sys
 import os
 import cv2
+from PyQt5.QtWidgets import QWidget, QVBoxLayout, QLabel, QHBoxLayout
+from PyQt5.QtCore import Qt
+from PyQt5.QtGui import QPixmap, QImage
 
-# Add the DevCode directory to the Python path
 sys.path.append(os.path.abspath("C:\\Users\\skais\\ThesisProject\\DevCode"))
 from Algorithms.Objects.colour_detection import ColourRecognizer
 from Algorithms.Objects.object_detection import ObjectRecognizer
 
-
 class GeneralDemoPage(QWidget):
+    """page used for general display of streams"""
     def __init__(self, title: str, description: str, algorithm: str):
         super().__init__()
         self.setWindowTitle(title)
@@ -65,7 +66,8 @@ def __init__(self, title: str, description: str, algorithm: str):
         self.failed_frames = 0  # Counter for consecutive failed frames
         self.timer = self.startTimer(20)
 
-    def timerEvent(self, event):
+    def timer_event(self):
+        """handle timer event for capturing and processing frames"""
         ret, frame = self.cap.read()
         if not ret:
             self.failed_frames += 1
@@ -86,15 +88,16 @@ def timerEvent(self, event):
             processed_frame = self.recognizer.detect_and_draw(frame)
 
         # Convert processed frame to QImage
-        height, width, channel = processed_frame.shape
+        height, width = processed_frame.shape
         bytes_per_line = 3 * width
         qimg = QImage(processed_frame.data, width, height, bytes_per_line, QImage.Format_BGR888)
 
         # Update the video frame (using QLabel's setPixmap)
         pixmap = QPixmap.fromImage(qimg)
         self.video_label.setPixmap(pixmap)
 
-    def closeEvent(self, event):
+    def close_event(self, event):
+        """handle close event to release resources"""
         # Release the video capture and stop the algorithm
         if hasattr(self, 'cap') and self.cap.isOpened():
             self.cap.release()

App/pages/hand_gesture_page.py

@@ -1,18 +1,16 @@
-# hand_gesture_page.py
+"""page for hand gesture detection, opened from home_page.py button"""
+import sys
+import os
+import cv2
 from PyQt5.QtWidgets import QWidget, QVBoxLayout, QLabel, QHBoxLayout
 from PyQt5.QtGui import QPixmap, QImage
 from PyQt5.QtCore import QTimer, Qt
 
-import sys
-import os
-
-# Add the DevCode directory to the Python path
 sys.path.append(os.path.abspath("C:\\Users\\skais\\ThesisProject\\DevCode"))
 from Algorithms.Body.hand_gesture_test import GestureRecognizer
 
-import cv2
-
 class HandGestureRecognitionPage(QWidget):
+    """Hand gesture recognizer"""
     def __init__(self):
         super().__init__()
         icon_paths = {
@@ -32,6 +30,7 @@ def __init__(self):
         self.timer.start(30)
 
     def setup_ui(self):
+        """UI for hand page setup"""
         self.setWindowTitle("Hand Gesture Recognition")
         self.setGeometry(100, 100, 1200, 600)
 
@@ -70,13 +69,15 @@ def setup_ui(self):
         self.setLayout(main_layout)
 
     def update_frame(self):
+        """update the video feed and hand gesture icons"""
         result = self.gesture_recognizer.process_frame()
         if result:
             self.video_feed.setPixmap(self._convert_cv_to_qt(result.main_frame))
             self.left_emoji.setPixmap(self._convert_cv_to_qt(result.left_emoji))
             self.right_emoji.setPixmap(self._convert_cv_to_qt(result.right_emoji))
 
     def _convert_cv_to_qt(self, cv_img):
+        """convert cv2 img to qpixmap for display in qlabel"""
         if cv_img is None:
             return QPixmap()
         if len(cv_img.shape) == 2:  # Grayscale
@@ -89,19 +90,7 @@ def _convert_cv_to_qt(self, cv_img):
         return QPixmap.fromImage(qt_image)
 
 
-    def closeEvent(self, event):
+    def close_event(self, event):
+        """handle close event to release resources"""
         self.gesture_recognizer.release()
         event.accept()
-"""
-def main():
-    from PyQt5.QtWidgets import QApplication
-    import sys
-
-    app = QApplication(sys.argv)
-    window = HandGestureRecognitionPage()
-    window.show()
-    sys.exit(app.exec_())
-
-if __name__ == '__main__':
-    main()
-    """