エラーメッセージ
```Traceback(most recent call last):
File "/home/pi/Desktop/ca.py",line36, in <module>
h, w = image.shape[:2]
AttributeError: 'NoneType' object has no attribute 'shape'
### 該当のソースコード
```import time
import picamera
from datetime import datetime
with picamera.PiCamera() as camera:
camera.resolution = (1024, 768)
camera.start_preview()
time.sleep(2)
timestr = datetime.now().strftime('%Y%m%d%H%M%S')
camera.capture(timestr+'.jpg')
import numpy as np
import time
import cv2
import os
DARKNET_PATH = 'yolo'
# Read labels that are used on object
labels = open(os.path.join(DARKNET_PATH, "data", "coco.names")).read().splitlines()
# Make random colors with a seed, such that they are the same next time
np.random.seed(0)
colors = np.random.randint(0, 255, size=(len(labels), 3)).tolist()
# Give the configuration and weight files for the model and load the network.
net = cv2.dnn.readNetFromDarknet(os.path.join(DARKNET_PATH, "cfg", "yolov3-tiny.cfg"), "yolov3-tiny.weights")
# Determine the output layer, now this piece is not intuitive
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# Load the image
image = cv2.imread(os.path.join(DARKNET_PATH, "Desktop", "timestr+'.jpg"))
# Get the shape
h, w = image.shape[:2]
# Load it as a blob and feed it to the network
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416), swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
# Get the output
layer_outputs = net.forward(ln)
end = time.time()
# Initialize the lists we need to interpret the results
boxes = []
confidences = []
class_ids = []
# Loop over the layers
for output in layer_outputs:
# For the layer loop over all detections
for detection in output:
# The detection first 4 entries contains the object position and size
scores = detection[5:]
# Then it has detection scores - it takes the one with maximal score
class_id = np.argmax(scores).item()
# The maximal score is the confidence
confidence = scores[class_id].item()
# Ensure we have some reasonable confidence, else ignorre
if confidence > 0.3:
# The first four entries have the location and size (center, size)
# It needs to be scaled up as the result is given in relative size (0.0 to 1.0)
box = detection[0:4] * np.array([w, h, w, h])
center_x, center_y, width, height = box.astype(int).tolist()
# Calculate the upper corner
x = center_x - width//2
y = center_y - height//2
# Add our findings to the lists
boxes.append([x, y, width, height])
confidences.append(confidence)
class_ids.append(class_id)
# Only keep the best boxes of the overlapping ones
idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.3, 0.3)
# Ensure at least one detection exists - needed otherwise flatten will fail
if len(idxs) > 0:
# Loop over the indexes we are keeping
for i in idxs.flatten():
# Get the box information
x, y, w, h = boxes[i]
# Make a rectangle
cv2.rectangle(image, (x, y), (x + w, y + h), colors[class_ids[i]], 2)
# Make and add text
text = "{}: {:.4f}".format(labels[class_ids[i]], confidences[i])
cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
0.5, colors[class_ids[i]], 2)
# Write the image with boxes and text
cv2.imwrite("timestr+'.png", image)
Q&A
