import cv2 as cv
import numpy as np
img = cv.imread("photo1.jpg")
print(img)
img_width = img.shape[1]
img_height = img.shape[0]
img_blob = cv.dnn.blobFromImage(img,1/255,(416,416),swapRB = True)
lables = ["person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat",
"trafficlight", "firehydrant", "stopsign", "parkingmeter", "bench", "bird", "cat",
"dog", "horse", "sheep", "cow", "elephant", "bear", "zebra", "giraffe", "backpack",
"umbrella", "handbag", "tie", "suitcase", "frisbee", "skis", "snowboard", "sportsball",
"kite", "baseballbat", "baseballglove", "skateboard", "surfboard", "tennisracket",
"bottle", "wineglass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
"sandwich", "orange", "broccoli", "carrot", "hotdog", "pizza", "donut", "cake", "chair",
"sofa", "pottedplant", "bed", "diningtable", "toilet", "tvmonitor", "laptop", "mouse",
"remote", "keyboard", "cellphone", "microwave", "oven", "toaster", "sink", "refrigerator",
"book", "clock", "vase", "scissors", "teddybear", "hairdrier", "toothbrush"]
colors = ["255,255,0","0,255,0","255,0,255","0,255,255","0,0,255"]
colors = [np.array(color.split(",")).astype("int") for color in colors]
colors = np.array(colors)
colors = np.tile(colors,(20,1))
model = cv.dnn.readNetFromDarknet ("Model/yolov3.cfg","Model/yolov3.weights")
layers = model.getLayerNames()
output_layer = [layers[layer[0]-1] for layer in model.getUnconnectedOutLayers()]
model.setInput(img_blob)
detection_layers = model.forward(output_layer)
ids_list = []
boxes_list = []
confidences_list = []
for detection_layer in detection_layers:
for object_detection in detection_layer:
scores = object_detection[5:]
predicted_id = np.argmax(scores)
confidence = scores[predicted_id]
if confidence > 0.80:
label = lables[predicted_id]
bounding_box = object_detection[0:4] * np.array(img_width,img_height,img_width,img_height)
(box_center_x, box_center_y,box_width,box_height) = bounding_box.astype("int")
start_x = int(box_center_x - (box_width/2))
start_y = int(box_center_y - (box_height/2))
ids_list.append(predicted_id)
confidences_list.append(float(confidence))
boxes_list.append([start_x,start_y,int(box_width),int(box_height)])
max_ids = cv.dnn.NMSBoxes(boxes_list,confidences_list,0.5,0.4)
for max_id in max_ids:
max_class_id = max_id[0]
box = boxes_list[max_class_id]
start_x = box[0]
start_y = box[1]
box_width = box[2]
box_height = box[3]
predicted_id = ids_list[max_class_id]
label = lables[predicted_id]
confidence = confidences_list[max_class_id]
end_x = start_x + box_width
end_y = start_y + box_height
box_color = colors[predicted_id]
box_color = [int(each)for each in box_color]
cv.rectangle(img,(start_x,start_y),(end_x,end_y),box_color,2)
cv.putText(img,label,(start_x,start_y-20),cv.FONT_HERSHEY_SIMPLEX,0.5,box_color,1)
cv.imshow("Detection Screen", img)
我在这段代码中得到以下错误:
Traceback (most recent call last):
File "C:UsersbahacOneDriveDesktopOpenCVmain.py", line 29, in <module>
output_layer = [layers[layer[0]-1] for layer in model.getUnconnectedOutLayers()]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "C:UsersbahacOneDriveDesktopOpenCVmain.py", line 29, in <listcomp>
output_layer = [layers[layer[0]-1] for layer in model.getUnconnectedOutLayers()]
~~~~~^^^
IndexError: invalid index to scalar variable.
我正在用Python研究OpenCV中的对象识别，但我在代码中遇到了错误。
你能帮我一下吗?问题似乎出在"layer"(或它的第一个元素)。我建议首先获取model.getUnconnectedOutLayers()的返回值，然后在列表推导中使用它之前验证它的内容，这使得调试更加困难。