def __init__(self, video_path):
self.cap = cv2.VideoCapture(video_path)
self.height, self.width = int(self.cap.get(cv2.CAP_PROP_FRAME_HEIGHT)), int(
self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
self.resize_size = (int(self.width * resize_ratio), int(self.height * resize_ratio))
self.IP = HumanDetection(self.resize_size)
if opt.out_video_path:
self.out = cv2.VideoWriter(opt.out_video_path, fourcc, 10, store_size)
def __init__(self, path, queueSize=3000):
self.path = path
# self.video = UMatFileVideoStream(self.path, 128).start()
# self.rgb = cv2.UMat(self.height, self.width, cv2.CV_8UC3)
self.cap = cv2.VideoCapture(path)
self.stopped = False
# # initialize the queue used to store frames read from
# # the video file
self.Q = Queue(maxsize=queueSize)
self.fgbg = cv2.createBackgroundSubtractorMOG2(history=500,
varThreshold=200,
detectShadows=False)
self.height, self.width = int(self.cap.get(
cv2.CAP_PROP_FRAME_HEIGHT)), int(
self.cap.get(cv2.CAP_PROP_FRAME_WIDTH))
self.resize_size = (int(self.width * resize_ratio),
int(self.height * resize_ratio))
self.IP = HumanDetection(self.resize_size)
from src.human_detection import HumanDetection
import cv2
import os
from config.config import img_folder, gray
import numpy as np
IP = HumanDetection()
if __name__ == '__main__':
src_folder = img_folder
dest_folder = src_folder + "_cut"
os.makedirs(dest_folder, exist_ok=True)
cnt = 0
for img_name in os.listdir(src_folder):
cnt += 1
print("Processing pic {}".format(cnt))
frame = cv2.imread(os.path.join(src_folder, img_name))
kps, boxes, _ = IP.process_img(frame, gray=gray)
img_rgb, img_black = IP.visualize()
# cv2.imwrite(os.path.join(dest_folder, img_name), img)
frame = img_black
if boxes is not None:
for idx, box in enumerate(boxes):
x1, y1, x2, y2 = int(box[0]), int(box[1]), int(box[2]), int(
box[3])
x1 = 0 if x1 < 0 else x1
y1 = 0 if y1 < 0 else y1
x2 = frame.shape[1] if x2 > frame.shape[1] else x2
y2 = frame.shape[0] if y2 > frame.shape[0] else y2
img = np.asarray(frame[y1:y2, x1:x2])
cv2.imshow("img", img)