refactor face detection code

Facial_Recognition_Software.py

@@ -1,55 +1,58 @@
+import sys
+import os
+
 import cv2
 import numpy as np
 
-import sys,os,numpy
 from glob import glob
 from skimage import io
 
-#read test photo 
-pwd  =  sys.path[0]
-img = cv2.imread(pwd + "/Facial_test_images/6.jpg")
 
-grey = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) 
+def face_detector_haarcascade(image):
 
-resize_fx = 1
-resize_fy = 1
+    grey = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) 
 
-grey = cv2.resize(grey, dsize=None, fx=resize_fx, fy=resize_fy, interpolation = cv2.INTER_AREA)
+    resize_fx = 1
+    resize_fy = 1
+    grey = cv2.resize(grey, dsize=None, fx=resize_fx, fy=resize_fy, interpolation = cv2.INTER_AREA)
 
+    pwd = sys.path[0]
+    classfier = cv2.CascadeClassifier(pwd + "/Facial_models/haarcascade_frontalface_alt2.xml")
 
-classfier = cv2.CascadeClassifier(pwd + "/Facial_models/haarcascade_frontalface_alt2.xml")
+    faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=1, minSize=(16, 16)) 
 
-faceRects = classfier.detectMultiScale(grey, scaleFactor=1.2, minNeighbors=1, minSize=(16, 16)) 
-
-color = (0, 255, 0)
-if len(faceRects) > 0:
+    if len(faceRects) > 0:
         for faceRect in faceRects:
             x, y, w, h = faceRect
             x = int(x/resize_fx)
             y = int(y/resize_fy)
             w = int(w/resize_fx)
             h = int(h/resize_fy) 
-        cv2.rectangle(img, (x - 10, y - 10), (x + w + 10, y + h + 10), color, 5) 
+            cv2.rectangle(image, (x - 10, y - 10), (x + w + 10, y + h + 10), (0, 255, 0), 5) 
 
-cv2.imwrite(pwd + "/Facial_test_images/output-a.jpg",img) 
-cv2.imshow("face_image_a",img)
+    return image
 
 
+def face_detector_ssd(image):
 
-image =  cv2.imread(pwd + "/Facial_test_images/6.jpg")
+    pwd = sys.path[0]
+    net = cv2.dnn.readNetFromCaffe(pwd+"/Facial_models/deploy.prototxt", pwd+"/Facial_models/res10_300x300_ssd_iter_140000_fp16.caffemodel")
 
-net = cv2.dnn.readNetFromCaffe(pwd+"/Facial_models/deploy.prototxt", pwd+"/Facial_models/res10_300x300_ssd_iter_140000_fp16.caffemodel")
+    resize = (800, 800)
+    confidence_thres = 0.65
     
-blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
+    blob = cv2.dnn.blobFromImage(cv2.resize(image, dsize=resize), 1.0, resize, (104.0, 177.0, 123.0))
+   # blob = cv2.dnn.blobFromImage(cv2.resize(image, (300, 300)), 1.0, (300, 300), (104.0, 177.0, 123.0))
 
-net.setInput(blob)
+    net.setInput(blob)
 
-detections = net.forward()
+    detections = net.forward()
 
-h,w,c=image.shape
-for i in range(0, detections.shape[2]):
+    h,w,c=image.shape
+
+    for i in range(0, detections.shape[2]):
         confidence = detections[0, 0, i, 2]
-    if confidence > 0.65:
+        if confidence > confidence_thres:
             box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
             (startX, startY, endX, endY) = box.astype("int")
             text = "{:.2f}%".format(confidence * 100)
@@ -57,6 +60,30 @@ for i in range(0, detections.shape[2]):
             cv2.rectangle(image, (startX, startY), (endX, endY),(0, 255,0), 5)
             cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 1.00, (0, 255, 0), 3)
     
-cv2.imwrite(pwd + "/Facial_test_images/output-b.jpg", image) 
-cv2.imshow("face_image_b",image)
-cv2.waitKey(0)
+    return image
+
+
+
+if __name__=="__main__":
+
+    image_name = "8.jpg"
+    split_name = image_name.split(".")
+
+    image_read_path = sys.path[0]+"/Facial_test_images/"+image_name
+    image_save_path = sys.path[0]+"/Facial_test_images/output/"+split_name[0]+"_result."+split_name[1]
+
+    image = cv2.imread(image_read_path)
+
+    image = face_detector_ssd(image)
+    #image = face_detector_haarcascade(image)
+
+    print(image_save_path)
+
+    cv2.imwrite(image_save_path, image)
+    cv2.imshow("result", image)
+    cv2.waitKey()
+    cv2.destroyAllWindows()
+
+    
+
+