From 57f0f1d1643e163c10aaf1a986442da2f7a1a62e Mon Sep 17 00:00:00 2001 From: Dan Wei Zuo Date: Sun, 15 Oct 2023 14:30:56 -0400 Subject: [PATCH] Pose Estimation - Update requirements for Pose Estimation - Add comments - Clean code (get rid of prints, format output json) --- .gitignore | 2 +- requirements.txt | 14 +++-- src/pose_estimation/pose_estimate.py | 90 ++++++++++++++++----------- tmp/court_video.mp4 | Bin 739684 -> 718253 bytes 4 files changed, 65 insertions(+), 41 deletions(-) diff --git a/.gitignore b/.gitignore index 4e3797dd..d60b7644 100644 --- a/.gitignore +++ b/.gitignore @@ -2,7 +2,7 @@ venv .env __pycache__ yolov8m-pose.pt -*.mp4 # ignores all .mp4 files; remove this line if some .mp4 files should be tracked +*.mp4 ball/lib/python3.11/site-packages/torch/lib/libtorch_cpu.dylib venv/lib/python3.11/site-packages/torch/lib/libtorch_cpu.dylib ball/ diff --git a/requirements.txt b/requirements.txt index 80ce400d..d3cfad92 100644 --- a/requirements.txt +++ b/requirements.txt @@ -25,8 +25,8 @@ opencv-python==4.7.0.72 matplotlib>=3.2.2 Pillow>=7.1.2 PyYAML>=5.3.1 -torch==2.0.1 -torchvision==0.15.2 +torch==2.0.1 # Check if this version exists, if not, use the latest stable version +torchvision==0.15.2 # Same check as torch tqdm>=4.41.0 seaborn scipy @@ -46,7 +46,13 @@ imageio # View streamlit>=1.18.1 -hydralit_components>= 1.0.10 +hydralit_components>=1.0.10 # Misc -pylint \ No newline at end of file +pylint + +# Additional Dependencies for Pose Estimation +json5 +ultralytics +imageio==2.9.0 +imageio-ffmpeg>=0.4.3 \ No newline at end of file diff --git a/src/pose_estimation/pose_estimate.py b/src/pose_estimation/pose_estimate.py index f2da19b8..9523ccb4 100644 --- a/src/pose_estimation/pose_estimate.py +++ b/src/pose_estimation/pose_estimate.py @@ -4,100 +4,118 @@ import torch import math import json -from ultralytics import YOLO - +from ultralytics import YOLO # Ensure ultralytics is installed and configured class PoseEstimator: def __init__(self, model_path='yolov8m-pose.pt', video_path='test_video.mp4', combinations=None): + # Initialize paths, model, and combinations of keypoints to calculate angles self.model_path = model_path self.video_path = video_path - self.model = YOLO(model_path) + self.model = YOLO(model_path) # Load the YOLO model + + # Adjusted combinations of points to calculate 8 angles self.combinations = combinations if combinations is not None else [ - (5, 7, 9), # Left arm: shoulder, elbow, wrist - (6, 8, 10), # Right arm: shoulder, elbow, wrist - (11, 13, 15), # Left leg: hip, knee, ankle - (12, 14, 16), # Right leg: hip, knee, ankle - (5, 6, 8), # Shoulder, right shoulder, right elbow (shoulder angle) - (6, 5, 7), # Right shoulder, left shoulder, left elbow - (11, 12, 14), # Hip, right hip, right knee - (12, 11, 13), # Right hip, left hip, left knee - (5, 11, 12), # Left shoulder, left hip, right hip - (6, 12, 11) # Right shoulder, right hip, left hip - ] + (5, 7, 9), (6, 8, 10), (11, 13, 15), (12, 14, 16), + (5, 6, 8), (6, 5, 7), (11, 12, 14), (12, 11, 13) + ] + + # Names corresponding to the adjusted 8 angle types for better understanding + self.angle_names = [ + "left_arm", "right_arm", "left_leg", "right_leg", + "shoulder_left_right_elbow", "shoulder_right_left_elbow", + "hip_left_right_knee", "hip_right_left_knee" + ] @staticmethod def compute_angle(p1, p2, p3): - vector_a = p1 - p2 - vector_b = p3 - p2 - - vector_a = vector_a / torch.norm(vector_a) - vector_b = vector_b / torch.norm(vector_b) + # Calculate angle given 3 points using the dot product and arc cosine + vector_a = p1 - p2 + vector_b = p3 - p2 - cosine_angle = torch.dot(vector_a, vector_b) - angle_radians = torch.acos(cosine_angle) + # Normalize the vectors (to make them unit vectors) + vector_a = vector_a / torch.norm(vector_a) + vector_b = vector_b / torch.norm(vector_b) - angle_degrees = angle_radians * 180 / math.pi + # Compute the angle + cosine_angle = torch.dot(vector_a, vector_b) + angle_radians = torch.acos(cosine_angle) + angle_degrees = angle_radians * 180 / math.pi - return angle_degrees + return angle_degrees def estimate_pose(self): - + # Start video capture cap = cv2.VideoCapture(self.video_path) + + # Initialize video writer writer = imageio.get_writer("tmp/test_result.mp4", mode="I") + # Initialize an empty list to store pose data pose_data = [] + # Names corresponding to angle types for better understanding angle_names = [ - "Left arm", "Right arm", "Left leg", "Right leg", - "Shoulder (L-R-E)", "Right shoulder (R-L-E)", "Hip (L-R-K)", - "Right hip (R-L-K)", "Left shoulder (L-H-RH)", "Right shoulder (R-H-LH)" + "left_arm", "right_arm", "left_leg", "right_leg", + "shoulder_left_right_elbow", "shoulder_right_left_elbow", + "hip_left_right_knee", "hip_right_left_knee", + "shoulder_left_hip_right_hip", "shoulder_right_hip_left_hip" ] while cap.isOpened(): + # Read frame-by-frame success, frame = cap.read() if success: + # Measure start time (to calculate FPS later) start_time = time.time() + + # Run pose estimation model on the frame results = self.model(frame, verbose=False) + + # Extract the keypoints keypoints = results[0].keypoints.xy + # Create annotated frame visualization annotated_frame = results[0].plot() + # Store frame number in data frame_pose_data = {'frame': cap.get(cv2.CAP_PROP_POS_FRAMES)} + # Calculate and display angles based on keypoint combinations for idx, combination in enumerate(self.combinations): - p1 = keypoints[0][combination[0]] - p2 = keypoints[0][combination[1]] - p3 = keypoints[0][combination[2]] - + p1, p2, p3 = keypoints[0][combination[0]], keypoints[0][combination[1]], keypoints[0][combination[2]] angle_degrees = self.compute_angle(p1, p2, p3) - print(f"{angle_names[idx]} Angle: {angle_degrees.item():.2f}°") - - frame_pose_data[f'angle_{idx}'] = angle_degrees.item() + # Add angle data to frame_pose_data + frame_pose_data[angle_names[idx]] = angle_degrees.item() + # Display angle on the annotated frame cv2.putText(annotated_frame, f"{angle_degrees:.2f}°", (int(p2[0]), int(p2[1])), cv2.FONT_HERSHEY_COMPLEX, 0.5, (255, 255, 0), 1, cv2.LINE_AA) + # Append frame's pose data to the list pose_data.append(frame_pose_data) + # Calculate and display FPS end_time = time.time() fps = 1 / (end_time - start_time) - cv2.putText(annotated_frame, f"FPS: {int(fps)}", (10, 50), cv2.FONT_HERSHEY_COMPLEX, 1.2, (255, 0, 255), 1, cv2.LINE_AA) + # Write annotated frame to output video annotated_frame = cv2.cvtColor(annotated_frame, cv2.COLOR_BGR2RGB) writer.append_data(annotated_frame) + # Break loop on 'q' key press if cv2.waitKey(1) & 0xFF == ord('q'): break else: break + # Cleanup and save data writer.close() cap.release() cv2.destroyAllWindows() with open("tmp/pose_data.json", "w") as f: - json.dump(pose_data, f) + json.dump(pose_data, f) \ No newline at end of file diff --git a/tmp/court_video.mp4 b/tmp/court_video.mp4 index 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