完整代码+训练好的手势识别器+测试图片

# -*- coding: utf-8 -*- """ Created on Wed Jul 12 15:31:05 2023 @author: zhaohao """ # -*- coding: utf-8 -*- """ Created on Wed Jul 12 15:28:16 2023 @author: zhaohao """ #@markdown We implemented some functions to visualize the gesture recognition results. <br/> Run the following cell to activate the functions. from matplotlib import pyplot as plt import mediapipe as mp from mediapipe.framework.formats import landmark_pb2 import math plt.rcParams.update({ 'axes.spines.top': False, 'axes.spines.right': False, 'axes.spines.left': False, 'axes.spines.bottom': False, 'xtick.labelbottom': False, 'xtick.bottom': False, 'ytick.labelleft': False, 'ytick.left': False, 'xtick.labeltop': False, 'xtick.top': False, 'ytick.labelright': False, 'ytick.right': False }) mp_hands = mp.solutions.hands mp_drawing = mp.solutions.drawing_utils mp_drawing_styles = mp.solutions.drawing_styles def display_one_image(image, title, subplot, titlesize=16): """Displays one image along with the predicted category name and score.""" plt.subplot(*subplot) plt.imshow(image) if len(title) > 0: plt.title(title, fontsize=int(titlesize), color='black', fontdict={'verticalalignment':'center'}, pad=int(titlesize/1.5)) return (subplot[0], subplot[1], subplot[2]+1) def display_batch_of_images_with_gestures_and_hand_landmarks(images, results): """Displays a batch of images with the gesture category and its score along with the hand landmarks.""" # Images and labels. images = [image.numpy_view() for image in images] gestures = [top_gesture for (top_gesture, _) in results] multi_hand_landmarks_list = [multi_hand_landmarks for (_, multi_hand_landmarks) in results] # Auto-squaring: this will drop data that does not fit into square or square-ish rectangle. rows = int(math.sqrt(len(images))) cols = len(images) // rows # Size and spacing. FIGSIZE = 13.0 SPACING = 0.1 subplot=(rows,cols, 1) if rows < cols: plt.figure(figsize=(FIGSIZE,FIGSIZE/cols*rows)) else: plt.figure(figsize=(FIGSIZE/rows*cols,FIGSIZE)) # Display gestures and hand landmarks. for i, (image, gestures) in enumerate(zip(images[:rows*cols], gestures[:rows*cols])): title = f"{gestures.category_name} ({gestures.score:.2f})" dynamic_titlesize = FIGSIZE*SPACING/max(rows,cols) * 40 + 3 annotated_image = image.copy() for hand_landmarks in multi_hand_landmarks_list[i]: 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( annotated_image, hand_landmarks_proto, mp_hands.HAND_CONNECTIONS, mp_drawing_styles.get_default_hand_landmarks_style(), mp_drawing_styles.get_default_hand_connections_style()) subplot = display_one_image(annotated_image, title, subplot, titlesize=dynamic_titlesize) # Layout. plt.tight_layout() plt.subplots_adjust(wspace=SPACING, hspace=SPACING) plt.show() # STEP 1: Import the necessary modules. import mediapipe as mp from mediapipe.tasks import python from mediapipe.tasks.python import vision # STEP 2: Create an GestureRecognizer object. base_options = python.BaseOptions(model_asset_path='gesture_recognizer.task') options = vision.GestureRecognizerOptions(base_options=base_options) recognizer = vision.GestureRecognizer.create_from_options(options) IMAGE_FILENAMES = ['thumbs_down.jpg', 'victory.jpg', 'thumbs_up.jpg', 'pointing_up.jpg'] images = [] results = [] for image_file_name in IMAGE_FILENAMES: # STEP 3: Load the input image. image = mp.Image.create_from_file(image_file_name) # STEP 4: Recognize gestures in the input image. recognition_result = recognizer.recognize(image) # STEP 5: Process the result. In this case, visualize it. images.append(image) top_gesture = recognition_result.gestures[0][0] hand_landmarks = recognition_result.hand_landmarks results.append((top_gesture, hand_landmarks)) display_batch_of_images_with_gestures_and_hand_landmarks(images, results)