File size: 4,223 Bytes
d3cd9d4 82de82c d3cd9d4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 |
from ultralytics import YOLO
from PIL import Image
import cv2
import numpy as np
# Constants for keypoint indices
NOSE = 0
LEFT_EYE = 1
RIGHT_EYE = 2
LEFT_EAR = 3
RIGHT_EAR = 4
LEFT_SHOULDER = 5
RIGHT_SHOULDER = 6
LEFT_ELBOW = 7
RIGHT_ELBOW = 8
LEFT_WRIST = 9
RIGHT_WRIST = 10
LEFT_HIP = 11
RIGHT_HIP = 12
LEFT_KNEE = 13
RIGHT_KNEE = 14
LEFT_ANKLE = 15
RIGHT_ANKLE = 16
connections = [
(LEFT_ANKLE, LEFT_KNEE),
(RIGHT_ANKLE, RIGHT_KNEE),
(LEFT_KNEE, LEFT_HIP),
(RIGHT_KNEE, RIGHT_HIP),
(LEFT_HIP, RIGHT_HIP),
(LEFT_HIP, LEFT_SHOULDER),
(RIGHT_HIP, RIGHT_SHOULDER),
(LEFT_SHOULDER, RIGHT_SHOULDER),
(LEFT_SHOULDER, LEFT_ELBOW),
(RIGHT_SHOULDER, RIGHT_ELBOW),
(LEFT_ELBOW, LEFT_WRIST),
(RIGHT_ELBOW, RIGHT_WRIST),
]
min_area = 15000
colors = [
(255, 0, 0),
(0, 255, 0),
(0, 0, 255),
(255, 255, 0),
(255, 0, 255),
(0, 255, 255)
]
def annotate_pose(image, keypoints, color):
"""
Add key points to the image for visualization.
"""
radius = 5
thickness = 2
# Enumerate through each tensor in keypoints
# and draw a circle for each keypoint
for i in range(len(keypoints)):
x, y = keypoints[i]
if i> 4 and x > 0 and y > 0:
cv2.circle(image, (int(x), int(y)), radius, color, thickness)
# Draw lines between keypoints
for connection in connections:
start_index, end_index = connection
start = keypoints[start_index]
end = keypoints[end_index]
if start[0] > 0 and start[1] > 0 and end[0] > 0 and end[1] > 0:
cv2.line(image, (int(start[0]), int(start[1])),
(int(end[0]), int(end[1])), color, 1)
def annotate_bounding_box(image, box, color):
"""
Draw bounding boxes around detected persons.
"""
x1, y1, x2, y2 = map(int, box)
cv2.rectangle(image, (x1, y1), (x2, y2), color, 2)
def area(box):
"""
Calculate the area of a bounding box.
"""
x1, y1, x2, y2 = box
return (x2 - x1) * (y2 - y1)
def process_pose_data(image_data):
"""
Process the image and return a dictionary with pose data.
"""
pose_data = {
"metadata": {
"model": "yolo11n-pose",
"version": "1.0",
"description": "Pose estimation data from YOLOv11n model."
},
"keypoints": [],
"bounding_boxes": []
}
numpy_image = np.array(image_data)
image = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR)
# Load a model
model = YOLO("weights/yolo11n-pose.pt") # load an official model
# Predict with the model
results = model(image) # predict on an image
# Access the results
for result in results:
xy = result.keypoints.xy # x and y coordinates
boxes = result.boxes.xyxy # bounding boxes
for i, person in enumerate(xy):
box = boxes[i] if i < len(boxes) else [0, 0, image.shape[1], image.shape[0]]
area_box = area(box)
if area_box < min_area:
continue
pose_data["keypoints"].append(person.tolist())
pose_data["bounding_boxes"].append(box.tolist())
return pose_data
def process_image(image_data):
"""
Process the image and return the annotated image.
"""
numpy_image = np.array(image_data)
image = cv2.cvtColor(numpy_image, cv2.COLOR_RGB2BGR)
# Load a model
model = YOLO("weights/yolo11n-pose.pt") # load an official model
# Predict with the model
results = model(image) # predict on an image
# Access the results
for result in results:
xy = result.keypoints.xy # x and y coordinates
boxes = result.boxes.xyxy # bounding boxes
for i, person in enumerate(xy):
box = boxes[i] if i < len(boxes) else [0, 0, image.shape[1], image.shape[0]]
area_box = area(box)
if area_box < min_area:
continue
annotate_pose(image, person, colors[i % len(colors)])
annotate_bounding_box(image, box, colors[i % len(colors)])
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) # Convert back to RGB
return Image.fromarray(image)
|