Add files via upload -- Improvements

faces-train.py

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+import cv2
+import os
+import numpy as np
+from PIL import Image
+import pickle
+
+BASE_DIR = os.path.dirname(os.path.abspath(__file__))
+image_dir = os.path.join(BASE_DIR, "images")
+
+face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt2.xml')
+recognizer = cv2.face.LBPHFaceRecognizer_create()
+
+current_id = 0
+label_ids = {}
+y_labels = []
+x_train = []
+files_to_remove = []
+dirs_to_remove = []
+
+for root, dirs, files in os.walk(image_dir):
+    i = 0
+    for file in files:
+        if len(files) == 0:
+            dirs_to_remove.append(root)
+        if file.lower().endswith("png") or file.lower().endswith("jpg") or file.lower().endswith("gif"):
+            path = os.path.join(root, file)
+            label = os.path.basename(root).replace(" ", "-").lower()
+            print(label, path)
+            if not label in label_ids:
+                label_ids[label] = current_id
+                current_id += 1
+            id_ = label_ids[label]
+            pil_image = Image.open(path).convert("L")  # grayscale
+            size = (320, 180)
+            final_image = pil_image.resize(size, Image.ANTIALIAS)
+            image_array = np.array(final_image, "uint8")
+            #Histrogram equalization
+            equalization = cv2.equalizeHist(image_array)
+            #Bilaterally filtered
+            filtered = cv2.bilateralFilter(equalization, 9, 10, 10)
+            faces = face_cascade.detectMultiScale(filtered, scaleFactor=1.05, minNeighbors=6)
+
+            if len(faces) != 0:
+                for (x, y, w, h) in faces:
+                    roi = filtered[y:y + h, x:x + w]
+                    x_train.append(roi)
+                    y_labels.append(id_)
+                    print("adding face for: " + label)
+            else:
+                files_to_remove.append(os.path.join(root, file))
+with open("pickles/face-labels.pickle", 'wb') as f:
+    pickle.dump(label_ids, f)
+
+recognizer.train(x_train, np.array(y_labels))
+recognizer.save("recognizers/face-trainner.yml")
+
+#cleanup unusuable images
+for fileLoc in files_to_remove:
+    print("deleting file: " + fileLoc)
+    #os.remove(fileLoc)
+for dirsLoc in dirs_to_remove:
+    print ("deleting file: " + dirsLoc)
+    # os.removedirs(dirsLoc)
+print ("removing " + str(len(files_to_remove)) + "files")

faces.py

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+import numpy as np
+import cv2
+import pickle
+from collections import Counter
+
+face_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_frontalface_alt.xml')
+eye_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_eye.xml')
+smile_cascade = cv2.CascadeClassifier('cascades/data/haarcascade_smile.xml')
+
+recognizer = cv2.face.LBPHFaceRecognizer_create()
+recognizer.read("recognizers/face-trainner.yml")
+
+labels = {"person_name": 1}
+with open("pickles/face-labels.pickle", 'rb') as f:
+    og_labels = pickle.load(f)
+    labels = {v: k for k, v in og_labels.items()}
+
+font = cv2.FONT_HERSHEY_SIMPLEX
+colorWhite = (255, 255, 255)
+colorRed = (0, 0, 255)
+colorYellow = (0, 200, 200)
+colorGreen = (0, 255, 0)
+thisColor = colorWhite
+stroke = 1
+result_array = []
+result_dict = {}
+result_interval = 15
+
+
+cap = cv2.VideoCapture(0)
+
+while (True):
+    # Capture frame-by-frame
+    ret, frame = cap.read()
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+    # Histrogram equalization
+    equalization = cv2.equalizeHist(gray)
+    # Bilaterally filtered
+    filtered = cv2.bilateralFilter(equalization, 9, 10, 10)
+    faces = face_cascade.detectMultiScale(filtered, scaleFactor=1.05, minNeighbors=6)
+
+    for (x, y, w, h) in faces:
+        # print(x,y,w,h)
+        roi_gray = filtered[y:y + h, x:x + w]  # (ycord_start, ycord_end)
+        roi_color = frame[y:y + h, x:x + w]
+
+        # recognize? deep learned model predict keras tensorflow pytorch scikit learn
+        id_, conf = recognizer.predict(roi_gray)
+        if 60 <= conf <= 100:
+
+            name = labels[id_]
+            if name == 'brandon' and conf >= 95:
+                thisColor = colorRed
+            elif 60 <= conf <= 80:
+                thisColor = colorYellow
+            elif 80 <= conf <= 99:
+                thisColor = colorGreen
+            else:
+                thisColor = colorWhite
+
+            if result_interval % 10 == 0:
+                if name not in result_dict:
+                    result_dict.update({name: [int(conf)]})
+                else:
+                    result_dict[name].append(int(conf))
+
+            stroke = 2
+            combined_name = name + " confidence: " + str(int(conf)) +"%"
+            cv2.putText(frame, combined_name, (x, y), font, .7, thisColor, stroke, cv2.LINE_AA)
+
+            if name == 'brandon' and conf >= 95:
+                thisColor = colorRed
+            else:
+                thisColor = colorWhite
+
+            end_cord_x = x + w
+            end_cord_y = y + h
+            cv2.rectangle(frame, (x, y), (end_cord_x, end_cord_y), thisColor, stroke)
+        else:
+            end_cord_x = x + w
+            end_cord_y = y + h
+            cv2.rectangle(frame, (x, y), (end_cord_x, end_cord_y), colorWhite, stroke)
+            cv2.putText(frame, "unknown", (x, y), font, .7, colorWhite, stroke, cv2.LINE_AA)
+
+    # Display the resulting frame
+    cv2.imshow('frame', frame)
+    result_interval += 1
+    if cv2.waitKey(20) & 0xFF == ord('q'):
+        break
+
+# When everything done, release the capture
+cap.release()
+cv2.destroyAllWindows()
+
+data = Counter(result_dict)
+conf_level_dict = dict(data.most_common(1))
+
+print(conf_level_dict)
+
+#keepers
+print(list(conf_level_dict.keys())[0])
+print(sum(list(conf_level_dict.values())[0]) / len(list(conf_level_dict.values())[0]))
+
+

image_capture.py

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+import cv2
+import os
+
+cap = cv2.VideoCapture(0)
+#update folder location for each user
+directory = "images/this.guy/"
+
+i = 0;
+while (True):
+    # Capture frame-by-frame
+    ret, frame = cap.read()
+    if i % 15 == 0:
+        if not os.path.exists(directory):
+            os.makedirs(directory)
+        cv2.imwrite(os.path.join(directory, 'this.guy'+str(i)+'.png'), frame)
+
+    # Display the resulting frame
+    cv2.imshow('frame', frame)
+    if cv2.waitKey(20) & 0xFF == ord('q'):
+        break
+    i+=1
+
+# When everything done, release the capture
+cap.release()
+cv2.destroyAllWindows()