import os
import numpy as np
import argparse
import cv2
# construct the argument parse and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-p", "--prototxt", default="googleNet\\deploy.prototxt",
help="path to Caffe 'deploy' prototxt file")
ap.add_argument("-m", "--model",
default="googleNet\\res10_300x300_ssd_iter_140000.caffemodel",
help="path to Caffe pre-trained model")
ap.add_argument("-c", "--confidence", type=float, default=0.5,
help="minimum probability to filter weak detections")
args = vars(ap.parse_args())
# load our serialized model from disk
print("[INFO] loading model...")
net = cv2.dnn.readNetFromCaffe(args["prototxt"], args["model"])
cap = cv2.VideoCapture("assets/fashion.mp4")
j = 0
while True:
ret, frame = cap.read()
w = int(cap.get(3))
h = int(cap.get(4))
blob = cv2.dnn.blobFromImage(cv2.resize(frame, (300, 300)), 1.0,
(300, 300), (104.0, 177.0, 123.0))
net.setInput(blob)
detections = net.forward()
# loop over the detections
for i in range(0, detections.shape[2]):
# extract the confidence (i.e., probability) associated with the
# prediction
confidence = detections[0, 0, i, 2]
# filter out weak detections by ensuring the `confidence` is
# greater than the minimum confidence
if confidence > args["confidence"]:
# compute the (x, y)-coordinates of the bounding box for the
# object
box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
(startX, startY, endX, endY) = box.astype("int")
# draw the bounding box of the face along with the associated
# probability
text = "{:.2f}%".format(confidence * 100)
y = startY - 10 if startY - 10 > 10 else startY + 10
cv2.rectangle(frame, (startX, startY), (endX, endY),
(0, 0, 255), 2)
cv2.putText(frame, text, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2)
cv2.imshow('frame', frame)
path = 'C:/Users/zchen/PycharmProjects/opencv/googleNet/record'
name = str(j) + ".jpg"
cv2.imwrite(os.path.join(path , name), frame)
j += 1
if cv2.waitKey(1) == ord('q'):
break
if cv2.waitKey(1) == ord('p'):
cv2.waitKey(-1) # wait until any key is pressed
cap.release()
cv2.destroyAllWindows()
-------------------
#video_writer.py
import os
import cv2
import glob
img_dict = {}
for filename in glob.glob('C:/Users/zchen/PycharmProjects/opencv/googleNet/record/*.jpg'):
img = cv2.imread(filename)
height, width, layers = img.shape
size = (width, height)
img_dict[filename.split("\\")[1]] = img
#print(img_dict)
print("loading image " + str(len(img_dict)))
path = 'C:/Users/zchen/PycharmProjects/opencv/googleNet'
out = cv2.VideoWriter(os.path.join(path , "fashion_face_detection.avi"), cv2.VideoWriter_fourcc(*'DIVX'), 60, size)
for i in range(len(img_dict)):
key = str(i) + ".jpg"
out.write(img_dict[key])
print("processing image " + str(i))
out.release()
googleNet
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