Add add gesture recognition

.gitignore

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+.venv
+*.jpg
+*.task

reconise.py

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+# Copyright 2023 The MediaPipe Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Main scripts to run gesture recognition."""
+
+import argparse
+import sys
+import time
+
+import cv2
+import mediapipe as mp
+
+from picamera2 import Picamera2
+
+from mediapipe.tasks import python
+from mediapipe.tasks.python import vision
+from mediapipe.framework.formats import landmark_pb2
+mp_hands = mp.solutions.hands
+mp_drawing = mp.solutions.drawing_utils
+mp_drawing_styles = mp.solutions.drawing_styles
+
+
+# Global variables to calculate FPS
+COUNTER, FPS = 0, 0
+START_TIME = time.time()
+
+
+def run(model: str, num_hands: int,
+        min_hand_detection_confidence: float,
+        min_hand_presence_confidence: float, min_tracking_confidence: float,
+        camera_id: int, width: int, height: int) -> None:
+  """Continuously run inference on images acquired from the camera.
+
+  Args:
+      model: Name of the gesture recognition model bundle.
+      num_hands: Max number of hands can be detected by the recognizer.
+      min_hand_detection_confidence: The minimum confidence score for hand
+        detection to be considered successful.
+      min_hand_presence_confidence: The minimum confidence score of hand
+        presence score in the hand landmark detection.
+      min_tracking_confidence: The minimum confidence score for the hand
+        tracking to be considered successful.
+      camera_id: The camera id to be passed to OpenCV.
+      width: The width of the frame captured from the camera.
+      height: The height of the frame captured from the camera.
+  """
+
+  # Start capturing video input from the camera
+
+  picam2 = Picamera2()
+  picam2.configure(picam2.create_preview_configuration(
+    main={"format": 'XRGB8888', "size": (1280, 720)}))
+  picam2.start()
+
+  # Visualization parameters
+  row_size = 50  # pixels
+  left_margin = 24  # pixels
+  text_color = (0, 0, 0)  # black
+  font_size = 1
+  font_thickness = 1
+  fps_avg_frame_count = 10
+
+  # Label box parameters
+  label_text_color = (255, 255, 255)  # white
+  label_font_size = 1
+  label_thickness = 2
+
+  recognition_frame = None
+  recognition_result_list = []
+
+  def save_result(result: vision.GestureRecognizerResult,
+                  unused_output_image: mp.Image, timestamp_ms: int):
+      global FPS, COUNTER, START_TIME
+
+      # Calculate the FPS
+      if COUNTER % fps_avg_frame_count == 0:
+          FPS = fps_avg_frame_count / (time.time() - START_TIME)
+          START_TIME = time.time()
+
+      recognition_result_list.append(result)
+      COUNTER += 1
+
+  # Initialize the gesture recognizer model
+  base_options = python.BaseOptions(model_asset_path=model)
+  options = vision.GestureRecognizerOptions(base_options=base_options,
+                                          running_mode=vision.RunningMode.LIVE_STREAM,
+                                          num_hands=num_hands,
+                                          min_hand_detection_confidence=min_hand_detection_confidence,
+                                          min_hand_presence_confidence=min_hand_presence_confidence,
+                                          min_tracking_confidence=min_tracking_confidence,
+                                          result_callback=save_result)
+  recognizer = vision.GestureRecognizer.create_from_options(options)
+
+  # Continuously capture images from the camera and run inference
+  while True:
+    image = picam2.capture_array()
+    
+    
+
+    image = cv2.flip(cv2.rotate(image, cv2.ROTATE_180),1)
+
+    # Convert the image from BGR to RGB as required by the TFLite model.
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
+    mp_image = mp.Image(image_format=mp.ImageFormat.SRGB, data=image)
+
+    # Run gesture recognizer using the model.
+    recognizer.recognize_async(mp_image, time.time_ns() // 1_000_000)
+
+    # Show the FPS
+    fps_text = 'FPS = {:.1f}'.format(FPS)
+    text_location = (left_margin, row_size)
+    current_frame = image
+    cv2.putText(current_frame, fps_text, text_location, cv2.FONT_HERSHEY_DUPLEX,
+                font_size, text_color, font_thickness, cv2.LINE_AA)
+
+    if recognition_result_list:
+      # Draw landmarks and write the text for each hand.
+      for hand_index, hand_landmarks in enumerate(
+          recognition_result_list[0].hand_landmarks):
+        # Calculate the bounding box of the hand
+        x_min = min([landmark.x for landmark in hand_landmarks])
+        y_min = min([landmark.y for landmark in hand_landmarks])
+        y_max = max([landmark.y for landmark in hand_landmarks])
+
+        # Convert normalized coordinates to pixel values
+        frame_height, frame_width = current_frame.shape[:2]
+        x_min_px = int(x_min * frame_width)
+        y_min_px = int(y_min * frame_height)
+        y_max_px = int(y_max * frame_height)
+
+        # Get gesture classification results
+        if recognition_result_list[0].gestures:
+          gesture = recognition_result_list[0].gestures[hand_index]
+          category_name = gesture[0].category_name
+          score = round(gesture[0].score, 2)
+          result_text = f'{category_name} ({score})'
+
+          # Compute text size
+          text_size = \
+          cv2.getTextSize(result_text, cv2.FONT_HERSHEY_DUPLEX, label_font_size,
+                          label_thickness)[0]
+          text_width, text_height = text_size
+
+          # Calculate text position (above the hand)
+          text_x = x_min_px
+          text_y = y_min_px - 10  # Adjust this value as needed
+
+          # Make sure the text is within the frame boundaries
+          if text_y < 0:
+            text_y = y_max_px + text_height
+
+          # Draw the text
+          cv2.putText(current_frame, result_text, (text_x, text_y),
+                      cv2.FONT_HERSHEY_DUPLEX, label_font_size,
+                      label_text_color, label_thickness, cv2.LINE_AA)
+
+        # Draw hand landmarks on the frame
+        hand_landmarks_proto = landmark_pb2.NormalizedLandmarkList()
+        hand_landmarks_proto.landmark.extend([
+          landmark_pb2.NormalizedLandmark(x=landmark.x, y=landmark.y,
+                                          z=landmark.z) for landmark in
+          hand_landmarks
+        ])
+        mp_drawing.draw_landmarks(
+          current_frame,
+          hand_landmarks_proto,
+          mp_hands.HAND_CONNECTIONS,
+          mp_drawing_styles.get_default_hand_landmarks_style(),
+          mp_drawing_styles.get_default_hand_connections_style())
+
+      recognition_frame = current_frame
+      recognition_result_list.clear()
+
+    if recognition_frame is not None:
+        recognition_frame = cv2.cvtColor(recognition_frame, cv2.COLOR_RGB2BGR)
+        cv2.imshow('gesture_recognition', recognition_frame)
+
+    # Stop the program if the ESC key is pressed.
+    if cv2.waitKey(1) == 27:
+        break
+
+  recognizer.close()
+  cap.release()
+  cv2.destroyAllWindows()
+
+
+def main():
+  parser = argparse.ArgumentParser(
+      formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+  parser.add_argument(
+      '--model',
+      help='Name of gesture recognition model.',
+      required=False,
+      default='gesture_recognizer.task')
+  parser.add_argument(
+      '--numHands',
+      help='Max number of hands that can be detected by the recognizer.',
+      required=False,
+      default=1)
+  parser.add_argument(
+      '--minHandDetectionConfidence',
+      help='The minimum confidence score for hand detection to be considered '
+           'successful.',
+      required=False,
+      default=0.5)
+  parser.add_argument(
+      '--minHandPresenceConfidence',
+      help='The minimum confidence score of hand presence score in the hand '
+           'landmark detection.',
+      required=False,
+      default=0.5)
+  parser.add_argument(
+      '--minTrackingConfidence',
+      help='The minimum confidence score for the hand tracking to be '
+           'considered successful.',
+      required=False,
+      default=0.5)
+  # Finding the camera ID can be very reliant on platform-dependent methods.
+  # One common approach is to use the fact that camera IDs are usually indexed sequentially by the OS, starting from 0.
+  # Here, we use OpenCV and create a VideoCapture object for each potential ID with 'cap = cv2.VideoCapture(i)'.
+  # If 'cap' is None or not 'cap.isOpened()', it indicates the camera ID is not available.
+  parser.add_argument(
+      '--cameraId', help='Id of camera.', required=False, default=0)
+  parser.add_argument(
+      '--frameWidth',
+      help='Width of frame to capture from camera.',
+      required=False,
+      default=640)
+  parser.add_argument(
+      '--frameHeight',
+      help='Height of frame to capture from camera.',
+      required=False,
+      default=480)
+  args = parser.parse_args()
+
+  run(args.model, int(args.numHands), args.minHandDetectionConfidence,
+      args.minHandPresenceConfidence, args.minTrackingConfidence,
+      int(args.cameraId), args.frameWidth, args.frameHeight)
+
+
+if __name__ == '__main__':
+  main()