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face_extractor_caffe.py

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+#Importing Required Libraries
+import os
+import cv2 
+import numpy as np
+import time
+
+#Starting time counter
+start = time.time()
+
+#Specifying paths of training data
+root_dir = 'trainset'
+
+#Specifying paths to Model architecture and Pretrained Weights
+prototxt_path = 'model_data/deploy.prototxt'
+caffemodel_path = 'model_data/weights.caffemodel'	
+
+#Empty Var for storing our Extracted Faces just for fun and info
+IMAGES = []
+
+#Implementing our pretrained DNN with Caffe Model for detecting frontal faces from our training data. 
+model = cv2.dnn.readNetFromCaffe(prototxt_path, caffemodel_path)
+
+i = 1
+
+#Iterating through directories & subdirectories and extracting the path of every jpg file present in "trainset" folder
+#Outcome would be that the Extracted Face inside a particular image will be written in the same destination with the same name to that of the original file
+for subdir, dirs,files in os.walk(root_dir):
+    for file in files:
+        #Reading/Loading the image
+        image = cv2.imread(os.path.join(subdir, file))
+        #Grabbing image dimensions
+        (h, w) = image.shape[:2]
+        #Resizing image to a specific dimension and creating a blob
+        blob = cv2.dnn.blobFromImage(cv2.resize(image, (256, 256)), 1.0, (256, 256), (104.0, 177.0, 123.0))
+        #Feeding the blob to our pretrained DNN
+        model.setInput(blob)
+        detections = model.forward()
+        #Looping through every single face detection in our image
+        for i in range(0, detections.shape[2]):
+            box = detections[0, 0, i, 3:7] * np.array([w, h, w, h])
+            #getting the dims of the extracted box containing our face
+            (startX, startY, endX, endY) = box.astype("int")
+            #grabbing the confidence value of the predicted box that its a face
+            confidence = detections[0, 0, i, 2]
+            #Only those Detections with more than 50% confidence will be extracted
+            if (confidence > 0.5):
+                #Slicing out the face
+                crop = image[startY:endY, startX:endX]
+                #Exception created for empty images. 
+                #7-10 recurrent empty images were found after image processing done above thus had to exclude them
+                try:
+                    #Saving the image in the same folder with same names
+                    #thus replacing our previous image and keeping the Extracted Face image only                    
+                    cv2.imwrite(os.path.join(subdir, file), crop)
+                    IMAGES.append(crop)
+                except:
+                    #exception of empty images that couldnt be written/saved. (Got 7-10 such images, couldnt understand why facing issue with this method)
+                    print(f"Number of Empty Ones found:-{i}")
+                    i+=1
+
+#Seizing Time Counter
+end = time.time()
+
+#Just some details for self understanding (Total Number of Faces extracted from the dataset, type of the variable 
+#and total time taken to process)
+print(len(IMAGES), type(IMAGES), ((start-end)/60))

face_extractor_cascade.py

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+#Importing Required Libraries
+import os
+import cv2 
+import time
+
+#Starting time counter
+start = time.time()
+
+#Specifying path of training data
+root_dir = 'trainset'
+
+#Empty List for storing our Extracted Faces
+IMAGES = []
+
+#Implementing our trained HaarClassifier for detecting frontal faces from our training data. 
+kascade = cv2.CascadeClassifier(cv2.data.haarcascades + "haarcascade_frontalface_default.xml")
+
+#Iterating through directories & subdirectories and extracting the path of every jpg file present in "trainset" folder
+#Outcome would be that the Extracted Face inside a particular image will be written in the same destination with the same name to that of the original file
+for subdir, dirs,files in os.walk(root_dir):
+    for file in files:
+        #Reading/Loading the image
+        img = cv2.imread(os.path.join(subdir, file))
+        #resizing the image but keeping aspect ratio of the image unchanged (this is with.respect.to HEIGHT)
+        #this is achieved by the formula (original width x new height)/ original height = new width
+        height = 256
+        width = img.shape[1]*height/img.shape[0]
+        img = cv2.resize(img, (int(width), height), None, 0.5, 0.5, interpolation=cv2.INTER_AREA)
+        #Conversion to a grayscale image
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        #Feeding the grayscale image to our Classifier
+        face = kascade.detectMultiScale(
+                gray,
+                scaleFactor=1.3,
+                minNeighbors=5
+                )
+        #HaarClassifier returned a 2D array, thus I'll extract these dimensions
+        #and use the same dims to crop out the portion from our original image. Hence preserving our color channels.
+        for (x,y,w,h) in face:
+            #Slicing out the face as mentioned above
+            crop = img[y:y+h,x:x+w]
+            #Saving the image in the same folder with same names
+            #thus replacing our previous image and keeping the Extracted Face image only            
+            cv2.imwrite(os.path.join(subdir, file), crop)
+            #Adding every extracted face to our empty list
+            IMAGES.append(crop)
+
+
+#Seizing Time Counter
+end = time.time()
+
+#Just some details for self understanding (Total Number of Faces extracted from the dataset, type of the variable 
+#and total time taken to process)
+print(len(IMAGES), type(IMAGES), ((start-end)/60))

face_extractor_fr.py

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+#Importing Required Libraries
+import os
+import time
+import face_recognition
+from PIL import Image
+
+#Starting time counter
+start = time.time()
+
+#Specifying path of training data
+root_dir = 'trainset'
+
+#Empty List for storing our Extracted Faces just for fun and info
+IMAGES = []
+
+#Iterating through directories & subdirectories and extracting the path of every jpg file present in "trainset" folder
+#Outcome would be that the Extracted Face inside a particular image will be written in the same destination with the same name to that of the original file
+for subdir, dirs,files in os.walk(root_dir):
+    for file in files:
+    #Loading the Image file, it is stored in the form of a numpy array.
+        img = face_recognition.load_image_file(os.path.join(subdir, file))
+        #Extracting the face locations from the array based image
+        face_locs = face_recognition.face_locations(img)
+        #face_recognition returns an array of the face locations, 
+        #hence I'll use the same dims to crop out the portion from our original image.
+        for (top, right, bottom, left) in face_locs:
+            crop = img[top:bottom, left:right]
+            #Convert the face image to a PIL-format image so that we can save it in the same folder with same names
+            #thus replacing our previous image and keeping the Extracted Face image only
+            pil_image = Image.fromarray(crop)
+            #saving the resulting face image
+            pil_image.save(os.path.join(subdir, file))
+            #Adding every resulting extracted face to our empty list
+            IMAGES.append(pil_image)
+
+#Seizing the counter
+end = time.time()
+#Just some details for self understanding (Total Number of Faces extracted from the dataset, type of the variable 
+#and total time taken to process)
+print(len(IMAGES), type(IMAGES), ((end-start)/60))