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yolo.py
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yolo.py
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# USAGE
# python yolo.py --image images/baggage_claim.jpg --yolo yolo-coco
# import the necessary packages
import numpy as np
import time
import cv2
import os
def detection_person_img(image, tiny=False):
# construct the argument parse and parse the arguments
# load the COCO class labels our YOLO model was trained on
labelsPath = os.path.sep.join(['yolo-coco/', "coco.names"])
LABELS = open(labelsPath).read().strip().split("\n")
# initialize a list of colors to represent each possible class label
np.random.seed(42)
COLORS = np.random.randint(0, 255, size=(len(LABELS), 3),
dtype="uint8")
# derive the paths to the YOLO weights and model configuration
if tiny:
weightsPath = os.path.sep.join(['yolo-coco/', "yolov3-tiny.weights"])
configPath = os.path.sep.join(['yolo-coco/', "yolov3-tiny.cfg"])
else:
weightsPath = os.path.sep.join(['yolo-coco/', "yolov3.weights"])
configPath = os.path.sep.join(['yolo-coco/', "yolov3.cfg"])
# load our YOLO object detector trained on COCO dataset (80 classes)
print("[INFO] loading YOLO from disk...")
net = cv2.dnn.readNetFromDarknet(configPath, weightsPath)
(H, W) = image.shape[:2]
# determine only the *output* layer names that we need from YOLO
ln = net.getLayerNames()
ln = [ln[i[0] - 1] for i in net.getUnconnectedOutLayers()]
# construct a blob from the input image and then perform a forward
# pass of the YOLO object detector, giving us our bounding boxes and
# associated probabilities
blob = cv2.dnn.blobFromImage(image, 1 / 255.0, (416, 416),
swapRB=True, crop=False)
net.setInput(blob)
start = time.time()
layerOutputs = net.forward(ln)
end = time.time()
# show timing information on YOLO
print("[INFO] YOLO took {:.6f} seconds".format(end - start))
# initialize our lists of detected bounding boxes, confidences, and
# class IDs, respectively
boxes = []
confidences = []
classIDs = []
# loop over each of the layer outputs
for output in layerOutputs:
# loop over each of the detections
for detection in output:
# extract the class ID and confidence (i.e., probability) of
# the current object detection
scores = detection[5:]
classID = np.argmax(scores)
# only person
if LABELS[classID] != 'person':
continue
confidence = scores[classID]
# filter out weak predictions by ensuring the detected
# probability is greater than the minimum probability
if confidence > 0.7: # todo: 设定阈值
# scale the bounding box coordinates back relative to the
# size of the image, keeping in mind that YOLO actually
# returns the center (x, y)-coordinates of the bounding
# box followed by the boxes' width and height
box = detection[0:4] * np.array([W, H, W, H])
(centerX, centerY, width, height) = box.astype("int")
# use the center (x, y)-coordinates to derive the top and
# and left corner of the bounding box
x = int(centerX - (width / 2))
y = int(centerY - (height / 2))
# update our list of bounding box coordinates, confidences,
# and class IDs
boxes.append([x, y, int(width), int(height)])
confidences.append(float(confidence))
classIDs.append(classID)
# apply non-maxima suppression to suppress weak, overlapping bounding
# boxes
idxs = cv2.dnn.NMSBoxes(boxes, confidences, 0.5,
0.3)
# ensure at least one detection exists
if len(idxs) > 0:
# loop over the indexes we are keeping
for i in idxs.flatten():
# extract the bounding box coordinates
(x, y) = (boxes[i][0], boxes[i][1])
(w, h) = (boxes[i][2], boxes[i][3])
# draw a bounding box rectangle and label on the image
color = [int(c) for c in COLORS[classIDs[i]]]
cv2.rectangle(image, (x, y), (x + w, y + h), color, 2)
text = "{}: {:.4f}".format(LABELS[classIDs[i]], confidences[i])
cv2.putText(image, text, (x, y - 5), cv2.FONT_HERSHEY_SIMPLEX,
0.5, color, 2)
return image
def detection_person(image_path: str, return_type="path"):
# load our input image and grab its spatial dimensions
image = cv2.imread(image_path)
detection_person_img(image)
# show the output image
#cv2.imshow("Image", image)
if return_type == "path":
outpath = image_path[0:image_path.rfind('.')] + "_out" + image_path[image_path.rfind('.'):]
#print(outpath)
cv2.imwrite(outpath, image)
return outpath
elif return_type == "img":
return image
if __name__ == '__main__':
detection_person('images/baggage_claim.jpg')