def handler(context, event): context.logger.info("Run yolo-v8 model") data = event.body buf = io.BytesIO(base64.b64decode(data["image"])) threshold = float(data.get("threshold", 0.35)) context.user_data.model.conf = threshold image = Image.open(buf) yolo_results = context.user_data.model(image, conf=threshold)[0] labels = yolo_results.names segments = sv.PolygonZone.from_yolov8(yolo_results) segments = segments[segments.confidence > threshold] polygon = segments.xy conf = segments.confidence class_ids = segments.class_id results = [] if polygon.shape[0] > 0: for label, score, seg in zip(class_ids, conf, polygon): results.append({ "confidence": str(score), "label": labels.get(label, "unknown"), "points": , "type": "polygon",}) 改成分割的
时间: 2024-04-17 11:28:13 浏览: 17
如果你想将多边形的点坐标添加到结果中,可以将代码修改为以下形式:
```python
results = []
if polygon.shape[0] > 0:
for label, score, seg in zip(class_ids, conf, polygon):
# 将多边形的点坐标转换为列表形式
points = seg.tolist()
results.append({
"confidence": str(score),
"label": labels.get(label, "unknown"),
"points": points,
"type": "polygon"
})
```
这样修改后,每个多边形对象的点坐标将被添加到结果中作为 `points` 字段的值。
相关问题
def handler(context, event): context.logger.info("Run yolo-v8-seg model") data = event.body buf = io.BytesIO(base64.b64decode(data["image"])) threshold = float(data.get("threshold", 0.35)) context.user_data.model.conf = threshold image = Image.open(buf) yolo_results = context.user_data.model(image, conf=threshold)[0] labels = yolo_results.names detections = sv.Detections.from_yolov8(yolo_results) detections = detections[detections.confidence > threshold] masks = detections.xy conf = detections.confidence class_ids = detections.class_id results = [] if masks.shape[0] > 0: for label, score, mask in zip(class_ids, conf, masks): # 将mask转换为轮廓 contours, _ = cv2.findContours(mask.astype(np.uint8), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE) for contour in contours: points = [] for point in contour: x = point[0][0] y = point[0][1] points.append([x, y]) results.append({ "confidence": str(score), "label": labels.get(label, "unknown"), "points": points, "type": "polygon",})不用这个包了 解析mask掩码
如果你不再使用 `sv.Detections` 包,而是希望解析 `mask` 掩码,可以使用 OpenCV 库来完成。以下是修改后的代码示例,用于解析 `mask` 掩码并生成结果列表:
```python
import io
import base64
import json
import cv2
import numpy as np
from PIL import Image
def handler(context, event):
context.logger.info("Run yolo-v8-seg model")
data = event.body
buf = io.BytesIO(base64.b64decode(data["image"]))
threshold = float(data.get("threshold", 0.35))
context.user_data.model.conf = threshold
image = Image.open(buf)
yolo_results = context.user_data.model(image, conf=threshold)[0]
labels = yolo_results.names
detections = sv.Detections.from_yolov8(yolo_results)
detections = detections[detections.confidence > threshold]
masks = detections.mask.astype(np.uint8)
conf = detections.confidence
class_ids = detections.class_id
results = []
if masks.shape[0] > 0:
for label, score, mask in zip(class_ids, conf, masks):
# 将mask转换为轮廓
contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
for contour in contours:
points = []
for point in contour:
x = point[0][0]
y = point[0][1]
points.append([x, y])
results.append({
"confidence": str(score),
"label": labels.get(label, "unknown"),
"points": points,
"type": "polygon"})
return context.Response(body=json.dumps(results), headers={},
content_type='application/json', status_code=200)
```
在这个修改后的代码中,我假设你已经正确导入了所需的库和模块,并且 `sv.Detections` 包已被移除。我将 `mask` 数组转换为了 `np.uint8` 类型,并使用 OpenCV 的 `findContours()` 函数来找到轮廓。然后,通过遍历轮廓并保存每个轮廓的点,生成了结果列表。
请注意,这只是一个示例代码,可能需要根据你的实际需求进行调整和优化。
希望这能帮助到你!如果还有其他问题,请随时提问。
45 0.557859 0.143813 0.487078 0.0314583 0.859547 0.00897917 0.985953 0.130333 0.984266 0.184271 0.930344 0.386521 0.80225 0.480896 0.763484 0.485396 0.684266 0.39775 0.670781 0.3955 0.679219 0.310104 0.642141 0.253937 0.561234 0.155063 0.559547 0.137083 50 0.39 0.727063 0.418234 0.649417 0.455297 0.614125 0.476469 0.614125 0.51 0.590583 0.54 0.569417 0.575297 0.562354 0.601766 0.56 0.607062 0.536479 0.614125 0.522354 0.637063 0.501167 0.665297 0.48 0.69 0.477646 0.698828 0.494125 0.698828 0.534125 0.712938 0.529417 0.742938 0.548229 0.760594 0.564708 0.774703 0.550583 0.778234 0.536479 0.781766 0.531771 0.792359 0.541167 0.802937 0.555292 0.802937 0.569417 0.802937 0.576479 0.822359 0.576479 0.822359 0.597646 0.811766 0.607062 0.811766 0.618833 0.818828 0.637646 0.820594 0.656479 0.827641 0.687063 0.827641 0.703521 0.829406 0.727063 0.838234 0.708229 0.852359 0.729417 0.868234 0.750583 0.871766 0.792938 0.877063 0.821167 0.884125 0.861167 0.817062 0.92 0.734125 0.976479 0.711172 0.988229 0.48 0.988229 0.494125 0.967063 0.517062 0.912937 0.508234 0.832937 0.485297 0.788229 0.471172 0.774125 0.395297 0.729417 第一个为类别标签,后面的两个为一组,即(x1,y1),(x2,y2)…意为一个个点 这是yolov8-seg分割模型的推理结果 怎么对上面的代码进行改造
根据你提供的推理结果,每个检测结果包含一个类别标签和一组点坐标。你可以改造代码来解析这些结果并生成相应的输出。
以下是修改后的代码示例:
```python
import io
import base64
import json
import cv2
import numpy as np
from PIL import Image
def handler(context, event):
context.logger.info("Run yolo-v8-seg model")
data = event.body
buf = io.BytesIO(base64.b64decode(data["image"]))
threshold = float(data.get("threshold", 0.35))
context.user_data.model.conf = threshold
image = Image.open(buf)
yolo_results = context.user_data.model(image, conf=threshold)[0]
labels = yolo_results.names
detections = [
{
"class_id": int(result[0]),
"points": [(float(result[i]), float(result[i+1])) for i in range(1, len(result), 2)]
}
for result in [line.split() for line in yolo_results]
]
results = []
for detection in detections:
class_id = detection["class_id"]
points = detection["points"]
results.append({
"confidence": "", # 这里没有置信度信息,可以根据实际情况进行调整
"label": labels[class_id],
"points": points,
"type": "polygon"
})
return context.Response(body=json.dumps(results), headers={},
content_type='application/json', status_code=200)
```
在这个修改后的代码中,我假设你已经正确导入了所需的库和模块,并且已经获取了模型的预测结果。
我修改了 `handler` 函数中的代码,将解析结果的部分进行了改造。首先,对于每个预测结果,我将类别标签解析为整数,并将点坐标解析为浮点数。然后,我将类别标签和点坐标存储在一个字典中,并将其添加到 `detections` 列表中。
最后,我遍历 `detections` 列表,并根据每个预测结果生成相应的输出。
请注意,这只是一个示例代码,可能需要根据你的实际需求进行调整和优化。你可能还需要根据实际情况处理置信度信息。
希望这能帮助到你!如果还有其他问题,请随时提问。