def run(self):
self.imageCount = 0
self.capture = cv2.VideoCapture(self.video_path)
while True:
ret, frame = self.capture.read()
if not ret:
break
im = darknet.nparray_to_image(frame)
boxes = darknet.detect(self.net, self.meta, im)
for i in range(len(boxes)):
score = boxes[i][1]
label = boxes[i][0]
xmin = boxes[i][2][0] - boxes[i][2][2] / 2
ymin = boxes[i][2][1] - boxes[i][2][3] / 2
xmax = boxes[i][2][0] + boxes[i][2][2] / 2
ymax = boxes[i][2][1] + boxes[i][2][3] / 2
cv2.rectangle(frame, (int(xmin), int(ymin)),
(int(xmax), int(ymax)), (0, 255, 0), 2)
cv2.putText(frame,
str(label), (int(xmin), int(ymin)),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.8,
color=(0, 255, 255),
thickness=2)
cv2.imwrite("./temps/temp" + str(self.imageCount) + ".jpg", frame)
self.add_image.emit("./temps/temp" + str(self.imageCount) + ".jpg")
time.sleep(0.05)
self.imageCount += 1
def videoDetection(self):
answer = QMessageBox.warning(
self, "注意", "如果您上次使用了摄像头检测或视频检测后没有导出，当前操作会导致数据丢失，请确认是否要继续！",
QMessageBox.No | QMessageBox.Yes, QMessageBox.Yes)
if answer == QMessageBox.No:
return
if os._exists("./temps"):
shutil.rmtree("./temps")
os.mkdir("./temps")
fname, _ = QFileDialog.getOpenFileName(self, '请选择图片文件', ".", "(*.mp4)")
fnameSplit = fname.split('.')
index = len(fnameSplit) - 1
typeSuffix = fnameSplit[index] #文件名后缀
if typeSuffix != "mp4":
QMessageBox.critical(self, "文件类型错误", "您未指定视频或指定的文件不是mp4文件，请确认！",
QMessageBox.Yes, QMessageBox.Yes)
else: #正确的图片路径
cap = cv2.VideoCapture(fname)
while True:
ret, frame = cap.read()
if ret:
im = darknet.nparray_to_image(frame)
r = darknet.detect(self.net, self.meta, im)
else:
break
cap.release()
cv2.destroyAllWindows()
def yolov3_detect():
t1 = time.time()
temp_jpg_file_path = "/home/li/webapi/tmp/"
image_seq = "default"
try:
image_seq = flask.request.form.get('sequence')
image_file = flask.request.files['image']
t2 = time.time() - t1
print(t2)
#print(image_seq)
#print(image_content)
if image_file:
temp_jpg_file = temp_jpg_file_path + "temp.jpg"
image_file.save(temp_jpg_file)
t2 = time.time() - t1
print(t2)
result = darknet.detect(yolov3_net,yolov3_meta,temp_jpg_file,thresh=.65)
return_json = {"sequence":image_seq,"result":result}
#print(return_json)
t2 = time.time() - t1
print(t2)
return flask.jsonify(return_json)
else:
error_json = {}
error_json["Error"] = "Image Error"
return flask.jsonify(error_json)
def predict(self, img_list=[]):
'''Detect and localize features in an image
Inputs
------
img_list: list
images to be analyzed
thresh: float
threshold confidence for detection
Outputs
-------
predictions: list
list of dicts
n images => n lists of dicts
per holo prediction:
{'conf': 50%, 'bbox': (x_centroid, y_centroid, width, height)}
'''
predictions = []
for image in img_list:
yolopred = darknet.detect(self.net, self.meta, image,
self.threshold, self.nms)
imagepreds = []
for pred in yolopred:
(label, conf, bbox) = pred
imagepreds.append({'label': label, 'conf': conf, 'bbox': bbox})
predictions.append(imagepreds)
return predictions
# Stupid python path shit.
# Instead just add darknet.py to somewhere in your python path
# OK actually that might not be a great idea, idk, work in progress
# Use at your own risk. or don't, i don't care
import sys, os
sys.path.append(os.path.join(os.getcwd(),'python/'))
import darknet as dn
import pdb
dn.set_gpu(0)
net = dn.load_net("cfg/yolo-thor.cfg", "/home/pjreddie/backup/yolo-thor_final.weights", 0)
meta = dn.load_meta("cfg/thor.data")
r = dn.detect(net, meta, "data/bedroom.jpg")
print r
# And then down here you could detect a lot more images like:
r = dn.detect(net, meta, "data/eagle.jpg")
print r
r = dn.detect(net, meta, "data/giraffe.jpg")
print r
r = dn.detect(net, meta, "data/horses.jpg")
print r
r = dn.detect(net, meta, "data/person.jpg")
print r
for i in range(meta.classes):
if probs[j][i] > 0:
res.append((meta.names[i], probs[j][i], (boxes[j].x, boxes[j].y, boxes[j].w, boxes[j].h)))
res = sorted(res, key=lambda x: -x[1])
dn.free_ptrs(dn.cast(probs, dn.POINTER(dn.c_void_p)), num)
return res
import sys, os
sys.path.append(os.path.join(os.getcwd(),'python/'))
import darknet as dn
# Darknet
net = dn.load_net("cfg/tiny-yolo.cfg", "tiny-yolo.weights", 0)
meta = dn.load_meta("cfg/coco.data")
r = dn.detect(net, meta, "data/dog.jpg")
print r
# scipy
arr= imread('data/dog.jpg')
im = array_to_image(arr)
r = detect2(net, meta, im)
print r
# OpenCV
arr = cv2.imread('data/dog.jpg')
im = array_to_image(arr)
dn.rgbgr_image(im)
r = detect2(net, meta, im)
print r
