def PedestrianDetection(img):
if cfg['view']['detection_method'].upper() == 'Darknet'.upper():
if not isOpencvImg(img):
img = PIL2Opencv(img)
elif cfg['view']['detection_method'].upper() == 'Keras'.upper():
if isOpencvImg(img):
img = Opencv2PIL(img)
boxes = np.array([])
if cfg['view'].getboolean('detect_person'):
boxes, scores = YOLO_model.detect_image(img)
return boxes
def put_boxes(img, boxes):
if not isOpencvImg(img):
img = PIL2Opencv(img)
# boxes
if len(boxes) > 0:
# loop over the indexes we are keeping
for i in range(len(boxes)):
# extract the bounding box coordinates
(x1, y1) = (boxes[i][0], boxes[i][1])
(x2, y2) = (boxes[i][2], boxes[i][3])
# draw a bounding box rectangle and label on the image
cv2.rectangle(img, (x1, y1), (x2, y2), (0, 0, 255), 2)
return img
def image():
if request.method == 'POST':
# Get the image from post request
img = base64_to_pil(request.json)
# 镜像翻转
# img = img.transpose(Image.FLIP_LEFT_RIGHT)
boxes = PedestrianDetection(img)
img = put_boxes(img, boxes)
if isOpencvImg(img):
img = Opencv2PIL(img)
result = pil_to_base64(img)
# Serialize the result, you can add additional fields
return jsonify(result=result)
return render_template('image_part.html')
def put_FPS_person(img, curr_fps, person_num):
if not isOpencvImg(img):
img = PIL2Opencv(img)
# FPS and person
text = "FPS: " + str(curr_fps) + "\nperson: " + str(person_num)
y0, dy = 15, 20
for i, txt in enumerate(text.split('\n')):
y = y0 + i * dy
# cv2.putText(img, txt, (50, y), cv2.FONT_HERSHEY_SIMPLEX, .6, (0, 255, 0), 1, 2)
cv2.putText(img,
text=txt,
org=(3, y),
fontFace=cv2.FONT_HERSHEY_SIMPLEX,
fontScale=0.50,
color=(255, 0, 0),
thickness=2)
# cv2.putText(img, text=text, org=(3, 15), fontFace=cv2.FONT_HERSHEY_SIMPLEX,
# fontScale=0.50, color=(255, 0, 0), thickness=2)
return img
def webcam_image_3():
global is_process_next
global last_time
if request.method == 'POST':
if not is_process_next:
return jsonify(result='')
# is_process_next = False
get_data = request.json
img = base64_to_pil(get_data['file'])
# print("get_data['camera_id'] : ", get_data['camera_id'])
if get_data['camera_id'] == 0:
# 左右镜像翻转
img = img.transpose(Image.FLIP_LEFT_RIGHT)
# YOLO
boxes = PedestrianDetection(img)
# img = PIL2Opencv(img)
# FPS
now_time = timer()
seconds = now_time - last_time
last_time = now_time
curr_fps = round(1.0 / seconds, 2)
tes_fps(curr_fps)
# img = put_FPS(img, curr_fps)
# img = put_boxes_FPS_person(img, boxes, curr_fps, len(boxes))
img = put_boxes(img, boxes)
img = put_FPS_person(img, curr_fps, len(boxes))
if isOpencvImg(img):
img = Opencv2PIL(img)
# img = Image.fromarray(cv2.cvtColor(img,cv2.COLOR_BGR2RGB))
result = pil_to_base64(img)
# Serialize the result, you can add additional fields
is_process_next = True
return jsonify(frame_id=get_data['frame_id'], result=result)
return jsonify(result='')
def video():
global percent_complete_frame
global num_frames
global elap
percent_complete_frame = 0
num_frames = 0
elap = 0
if request.method == "POST":
file = request.files["file"]
print("File uploaded")
print(file)
my_upload = storage.upload(file)
video_file_name = my_upload.name
video_file_extension = my_upload.extension
video_file = app.config.get("STORAGE_CONTAINER") + video_file_name
vid = cv2.VideoCapture(video_file)
if not vid.isOpened():
raise IOError("Couldn't open webcam or video")
num_frames = vid.get(cv2.CAP_PROP_FRAME_COUNT)
video_FourCC = int(vid.get(cv2.CAP_PROP_FOURCC))
video_fps = vid.get(cv2.CAP_PROP_FPS)
video_size = (int(vid.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(vid.get(cv2.CAP_PROP_FRAME_HEIGHT)))
outVideoName = video_file_name.split(
'.')[0] + "_process." + video_file_extension
output_path = app.config.get("STORAGE_CONTAINER") + outVideoName
out = cv2.VideoWriter(output_path, video_FourCC, video_fps, video_size)
while (vid.isOpened()):
ret, img = vid.read()
if ret == True:
# 计时
start = time.time()
# # # 简单尝试处理一下
# img = cv2.flip(img, 0)
# 调用 Keras 目标检测
boxes = PedestrianDetection(img)
img = put_boxes(img, boxes)
# PIL 转 opencv
if not isOpencvImg(img):
img = PIL2Opencv(img)
end = time.time()
elap = (end - start)
# write the flipped frame
out.write(img)
percent_complete_frame += 1
else:
break
vid.release()
out.release()
obj = storage.get(outVideoName)
view_url = obj.url
download_url = obj.download_url()
res = make_response(
jsonify({
"message": "Process Done",
'view_url': view_url,
'download_url': download_url
}), 200)
return res
return render_template("upload_video.html")