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行人检测
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‎ch10-图像上的算术运算/长方形2.py‎

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@@ -39,5 +39,5 @@ def get_euler_distance(pt1, pt2):
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cv2.imshow('src', img22)
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cv2.imshow('warp', warp)
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cv2.imwrite('crop0.jpg',warp)
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# cv2.imwrite('crop0.jpg',warp)
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cv2.waitKey(0)

‎ch51-对象检测-使用Haar分类器进行面部检测/行人检测/Pedestrian_Detection1.py‎

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"""
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Pedestrian_Detection1.py:
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http://www.pyimagesearch.com/2015/11/09/pedestrian-detection-opencv/
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运行
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python Pedestrian_Detection1.py --images images
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"""
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# import the necessary packages
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from __future__ import print_function
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from imutils.object_detection import non_max_suppression
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from imutils import paths
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import numpy as np
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import argparse
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import imutils
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import cv2
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# construct the argument parse and parse the arguments
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ap = argparse.ArgumentParser()
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ap.add_argument("-i", "--images", required=True, help="path to images directory")
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args = vars(ap.parse_args())
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# initialize the HOG descriptor/person detector
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hog = cv2.HOGDescriptor()
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hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
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# loop over the image paths
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for imagePath in paths.list_images(args["images"]):
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# load the image and resize it to (1) reduce detection time
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# and (2) improve detection accuracy
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image = cv2.imread(imagePath)
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image = imutils.resize(image, width=min(400, image.shape[1]))
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orig = image.copy()
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# detect people in the image
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(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
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padding=(8, 8), scale=1.05)
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# draw the original bounding boxes
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for (x, y, w, h) in rects:
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cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
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# apply non-maxima suppression to the bounding boxes using a
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# fairly large overlap threshold to try to maintain overlapping
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# boxes that are still people
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rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
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pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
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# draw the final bounding boxes
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for (xA, yA, xB, yB) in pick:
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cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
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# show some information on the number of bounding boxes
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filename = imagePath[imagePath.rfind("/") + 1:]
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print("[INFO] {}: {} original boxes, {} after suppression".format(
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filename, len(rects), len(pick)))
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# show the output images
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cv2.imshow("Before NMS", orig)
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cv2.imshow("After NMS", image)
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cv2.waitKey(0)

‎ch51-对象检测-使用Haar分类器进行面部检测/行人检测/Pedestrian_Detection_video.py‎

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"""
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Pedestrian_Detection_video.py:
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视频网站
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https://v.qq.com/x/page/t0501y6jtfi.html
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"""
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# import the necessary packages
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from __future__ import print_function
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from imutils.object_detection import non_max_suppression
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from imutils import paths
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import numpy as np
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import argparse
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import imutils
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import cv2
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import time
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# construct the argument parse and parse the arguments
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# ap = argparse.ArgumentParser()
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# ap.add_argument("-i", "--images", required=True, help="path to images directory")
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# args = vars(ap.parse_args())
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# initialize the HOG descriptor/person detector
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hog = cv2.HOGDescriptor()
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hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
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#
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cap = cv2.VideoCapture('videos/礼让斑马线！齐齐哈尔城市文明的伤！.mp4')
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fps = cap.get(cv2.CAP_PROP_FPS) # 25.0
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print("Frames per second using video.get(cv2.CAP_PROP_FPS) : {0}".format(fps))
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num_frames = cap.get(cv2.CAP_PROP_FRAME_COUNT)
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print('共有', num_frames, '帧') # 共有 2499.0 帧
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frame_height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
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frame_width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
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print('高：', frame_height, '宽：', frame_width) # 高： 480.0 宽： 640.0
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# exit(0)
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# 跳过多少帧
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skips = 20
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# loop over the image paths
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# for imagePath in paths.list_images(args["images"]):
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while cap.isOpened():
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# load the image and resize it to (1) reduce detection time
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# and (2) improve detection accuracy
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# image = cv2.imread(imagePath)
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ret, frame = cap.read()
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image = frame
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#
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current = cap.get(cv2.CAP_PROP_POS_FRAMES)
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if current % skips != 0:
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continue
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image = imutils.resize(image, width=min(400, image.shape[1]))
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orig = image.copy()
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# detect people in the image
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(rects, weights) = hog.detectMultiScale(image, winStride=(4, 4),
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padding=(8, 8), scale=1.05)
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# draw the original bounding boxes
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for (x, y, w, h) in rects:
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cv2.rectangle(orig, (x, y), (x + w, y + h), (0, 0, 255), 2)
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# apply non-maxima suppression to the bounding boxes using a
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# fairly large overlap threshold to try to maintain overlapping
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# boxes that are still people
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rects = np.array([[x, y, x + w, y + h] for (x, y, w, h) in rects])
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pick = non_max_suppression(rects, probs=None, overlapThresh=0.65)
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# draw the final bounding boxes
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for (xA, yA, xB, yB) in pick:
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cv2.rectangle(image, (xA, yA), (xB, yB), (0, 255, 0), 2)
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# show some information on the number of bounding boxes
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# filename = imagePath[imagePath.rfind("/") + 1:]
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# print("[INFO] {}: {} original boxes, {} after suppression".format(
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print("[INFO] {} original boxes, {} after suppression".format(len(rects), len(pick)))
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# show the output images
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cv2.imshow("Before NMS", orig)
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cv2.imshow("After NMS", image)
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cv2.moveWindow("After NMS", y=0, x=400)
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key = cv2.waitKey(delay=1)
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if key == ord("q"):
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break
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# When everything done, release the capture
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cap.release()
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cv2.destroyAllWindows()

‎ch51-对象检测-使用Haar分类器进行面部检测/行人检测/images/p1.jpg‎

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‎ch51-对象检测-使用Haar分类器进行面部检测/行人检测/images/p2.jpg‎

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‎ch51-对象检测-使用Haar分类器进行面部检测/行人检测/images/p3.jpg‎

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‎ch51-对象检测-使用Haar分类器进行面部检测/行人检测/videos/礼让斑马线！齐齐哈尔城市文明的伤！.mp4‎

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‎官方samples/peopledetect.py‎

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@@ -1,6 +1,9 @@
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#!/usr/bin/env python
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'''
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人类检测
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python peopledetect.py ../data/basketball1.png ../data/basketball2.png ../data/dnn/rgb.jpg
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example to detect upright people in images using HOG features
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Usage:
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draw_detections(img, found)
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draw_detections(img, found_filtered, 3)
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print('%d (%d) found' % (len(found_filtered), len(found)))
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cv2.imshow('img', img)
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cv2.imshow(fn, img)
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ch = cv2.waitKey()
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if ch == 27:
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break

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