refactor face detection code
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@ -1,27 +1,26 @@
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import sys
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import os
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import cv2
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import numpy as np
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import sys,os,numpy
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from glob import glob
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from skimage import io
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#read test photo
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pwd = sys.path[0]
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img = cv2.imread(pwd + "/Facial_test_images/6.jpg")
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grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
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def face_detector_haarcascade(image):
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grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
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resize_fx = 1
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resize_fy = 1
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grey = cv2.resize(grey, dsize=None, fx=resize_fx, fy=resize_fy, interpolation = cv2.INTER_AREA)
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pwd = sys.path[0]
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classfier = cv2.CascadeClassifier(pwd + "/Facial_models/haarcascade_frontalface_alt2.xml")
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faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=1, minSize=(16, 16))
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color = (0, 255, 0)
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if len(faceRects) > 0:
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for faceRect in faceRects:
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x, y, w, h = faceRect
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@ -29,27 +28,31 @@ if len(faceRects) > 0:
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y = int(y/resize_fy)
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w = int(w/resize_fx)
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h = int(h/resize_fy)
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cv2.rectangle(img, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 5)
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cv2.rectangle(image, (x - 10, y - 10), (x + w + 10, y + h + 10), (0, 255, 0), 5)
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cv2.imwrite(pwd + "/Facial_test_images/output-a.jpg",img)
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cv2.imshow("face_image_a",img)
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return image
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def face_detector_ssd(image):
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image = cv2.imread(pwd + "/Facial_test_images/6.jpg")
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pwd = sys.path[0]
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net = cv2.dnn.readNetFromCaffe(pwd+"/Facial_models/deploy.prototxt", pwd+"/Facial_models/res10_300x300_ssd_iter_140000_fp16.caffemodel")
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blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
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resize = (800, 800)
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confidence_thres = 0.65
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blob = cv2.dnn.blobFromImage(cv2.resize(image, dsize=resize), 1.0, resize, (104.0, 177.0, 123.0))
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# blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
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net.setInput(blob)
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detections = net.forward()
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h,w,c=image.shape
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for i in range(0, detections.shape[2]):
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confidence = detections[0, 0, i, 2]
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if confidence > 0.65:
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if confidence > confidence_thres:
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box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
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(startX, startY, endX, endY) = box.astype("int")
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text = "{:.2f}%".format(confidence * 100)
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@ -57,6 +60,30 @@ for i in range(0, detections.shape[2]):
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cv2.rectangle(image, (startX, startY), (endX, endY),(0, 255,0), 5)
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cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 1.00, (0, 255, 0), 3)
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cv2.imwrite(pwd + "/Facial_test_images/output-b.jpg", image)
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cv2.imshow("face_image_b",image)
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cv2.waitKey(0)
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return image
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if __name__=="__main__":
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image_name = "8.jpg"
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split_name = image_name.split(".")
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image_read_path = sys.path[0]+"/Facial_test_images/"+image_name
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image_save_path = sys.path[0]+"/Facial_test_images/output/"+split_name[0]+"_result."+split_name[1]
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image = cv2.imread(image_read_path)
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image = face_detector_ssd(image)
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#image = face_detector_haarcascade(image)
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print(image_save_path)
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cv2.imwrite(image_save_path, image)
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cv2.imshow("result", image)
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cv2.waitKey()
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cv2.destroyAllWindows()
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Before Width: | Height: | Size: 81 KiB After Width: | Height: | Size: 80 KiB |
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Facial_test_images/8.jpg
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Facial_test_images/9.jpg
Normal file
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Facial_test_images/output/3_result.jpg
Normal file
After Width: | Height: | Size: 159 KiB |
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Facial_test_images/output/3s_result.jpg
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After Width: | Height: | Size: 162 KiB |
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Facial_test_images/output/8_result.jpg
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After Width: | Height: | Size: 196 KiB |
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Facial_test_images/output/8s_result.jpg
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Facial_test_images/output/9_result.jpg
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Facial_test_images/output/9s_result.jpg
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