face_recognition

face_recognition/cv_test.py

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+import cv2
+
+cam = cv2.VideoCapture(0)
+
+while True:
+    check, frame = cam.read()
+    
+    cv2.imshow('video', frame)
+
+    key = cv2.waitKey(1)
+    if key == 27:
+        break
+
+cam.release()
+cv2.destroyAllWindows()

face_recognition/face_main.py

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+import face_recognition as face 
+import numpy as np 
+import cv2
+
+#ORIGINAL_CODE_CREDIT:  https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py
+
+#ดึงวิดีโอตัวอย่างเข้ามา, ถ้าต้องการใช้webcamให้ใส่เป็น
+video_capture = cv2.VideoCapture(0) 
+
+#ใบหน้าคนที่ต้องการรู้จำเป็นreference #คนที่1
+boom_image = face.load_image_file("boom.jpg")
+boom_face_encoding = face.face_encodings(boom_image)[0]
+
+tat_image = face.load_image_file("tat.jpg")
+tat_face_encoding = face.face_encodings(tat_image)[0]
+
+'''
+#ใบหน้าคนที่ต้องการรู้จำเป็นreference #คนที่2
+oat_image = face.load_image_file("oat.jpg")
+oat_face_encoding = face.face_encodings(oat_image)[0]
+'''
+
+
+#ประกาศตัวแปร
+face_locations = []
+face_encodings = []
+face_names = []
+face_percent = []
+#ตัวแปรนี้ใช้สำหรับคิดเฟรมเว้นเฟรมเพื่อเพิ่มfps 
+process_this_frame = True
+
+known_face_encodings = [boom_face_encoding, tat_face_encoding]
+known_face_names = ["BOOM", "TAT"]
+
+#loopคำนวณแต่ละเฟรมของวิดีโอ
+while True:
+    #อ่านค่าแต่ละเฟรมจากวิดีโอ
+    ret, frame = video_capture.read()
+    if ret:
+        #ลดขนาดสองเท่าเพื่อเพิ่มfps 
+        small_frame = cv2.resize(frame, (0,0), fx=0.5,fy=0.5)
+        #เปลี่ยน bgrเป็น rgb 
+        #rgb_small_frame = small_frame[:,:,::-1]
+        rgb_small_frame = np.ascontiguousarray(small_frame[:, :, ::-1])
+
+        face_names = []
+        face_percent = []
+
+        if process_this_frame:
+            #ค้นหาตำแหน่งใบหน้าในเฟรม 
+            face_locations = face.face_locations(rgb_small_frame)
+            #นำใบหน้ามาหาfeaturesต่างๆที่เป็นเอกลักษณ์ 
+            face_encodings = face.face_encodings(rgb_small_frame, face_locations)
+            
+            #เทียบแต่ละใบหน้า
+            for face_encoding in face_encodings:
+                face_distances = face.face_distance(known_face_encodings, face_encoding)
+                best = np.argmin(face_distances)
+                face_percent_value = 1-face_distances[best]
+
+                #กรองใบหน้าที่ความมั่นใจ50% ปล.สามารถลองเปลี่ยนได้
+                if face_percent_value >= 0.4:
+                    name = known_face_names[best]
+                    percent = round(face_percent_value*100,2)
+                    face_percent.append(percent)
+                else:
+                    name = "UNKNOWN"
+                    face_percent.append(0)
+                face_names.append(name)
+
+        #วาดกล่องและtextเมื่อแสดงผลออกมาออกมา
+        for (top,right,bottom, left), name, percent in zip(face_locations, face_names, face_percent):
+            top*= 2
+            right*= 2
+            bottom*= 2
+            left*= 2
+
+            if name == "UNKNOWN":
+                color = [46,2,209]
+            else:
+                color = [255,102,51]
+
+            cv2.rectangle(frame, (left,top), (right,bottom), color, 2)
+            cv2.rectangle(frame, (left-1, top -30), (right+1,top), color, cv2.FILLED)
+            cv2.rectangle(frame, (left-1, bottom), (right+1,bottom+30), color, cv2.FILLED)
+            font = cv2.FONT_HERSHEY_DUPLEX
+            cv2.putText(frame, name, (left+6, top-6), font, 0.6, (255,255,255), 1)
+            cv2.putText(frame, "MATCH: "+str(percent)+"%", (left+6, bottom+23), font, 0.6, (255,255,255), 1)
+
+
+        #สลับค่าเป็นค่าตรงข้ามเพื่อให้คิดเฟรมเว้นเฟรม
+        process_this_frame = not process_this_frame
+
+        #แสดงผลลัพท์ออกมา
+        cv2.imshow("Video", frame)
+        if cv2.waitKey(1) & 0xFF == ord('q'):
+            break
+
+
+#ล้างค่าต่างๆเมื่อปิดโปรแกรม
+video_capture.release()
+cv2.destroyAllWindows()

face_recognition/face_main_2.py

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+import face_recognition
+import cv2
+import numpy as np
+import matplotlib.pyplot as plt
+
+# This is a demo of running face recognition on live video from your webcam. It's a little more complicated than the
+# other example, but it includes some basic performance tweaks to make things run a lot faster:
+#   1. Process each video frame at 1/4 resolution (though still display it at full resolution)
+#   2. Only detect faces in every other frame of video.
+
+# PLEASE NOTE: This example requires OpenCV (the `cv2` library) to be installed only to read from your webcam.
+# OpenCV is *not* required to use the face_recognition library. It's only required if you want to run this
+# specific demo. If you have trouble installing it, try any of the other demos that don't require it instead.
+
+# Get a reference to webcam #0 (the default one)
+video_capture = cv2.VideoCapture(0)
+
+# Load a sample picture and learn how to recognize it.
+PUN_image = face_recognition.load_image_file("PUN.jpg")
+PUN_face_encoding = face_recognition.face_encodings(PUN_image)[0]
+
+# Load a second sample picture and learn how to recognize it.
+TAT_image = face_recognition.load_image_file("tat.jpg")
+TAT_face_encoding = face_recognition.face_encodings(TAT_image)[0]
+
+# Create arrays of known face encodings and their names
+known_face_encodings = [
+    PUN_face_encoding,
+    TAT_face_encoding
+]
+known_face_names = [
+    "PUN",
+    "TAT"
+]
+
+# Initialize some variables
+face_locations = []
+face_encodings = []
+face_names = []
+process_this_frame = True
+
+while True:
+    # Grab a single frame of video
+    ret, frame = video_capture.read()
+
+    # Only process every other frame of video to save time
+    if process_this_frame:
+        # Resize frame of video to 1/4 size for faster face recognition processing
+        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)
+
+        # Convert the image from BGR color (which OpenCV uses) to RGB color (which face_recognition uses)
+        rgb_small_frame = np.ascontiguousarray(small_frame[:, :, ::-1])
+        
+        # Find all the faces and face encodings in the current frame of video
+        face_locations = face_recognition.face_locations(rgb_small_frame)
+        face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)
+
+        face_names = []
+        for face_encoding in face_encodings:
+            # See if the face is a match for the known face(s)
+            matches = face_recognition.compare_faces(known_face_encodings, face_encoding)
+            name = "Unknown"
+
+            # # If a match was found in known_face_encodings, just use the first one.
+            # if True in matches:
+            #     first_match_index = matches.index(True)
+            #     name = known_face_names[first_match_index]
+
+            # Or instead, use the known face with the smallest distance to the new face
+            face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)
+            best_match_index = np.argmin(face_distances)
+            if matches[best_match_index]:
+                name = known_face_names[best_match_index]
+
+            face_names.append(name)
+
+    process_this_frame = not process_this_frame
+
+
+    # Display the results
+    for (top, right, bottom, left), name in zip(face_locations, face_names):
+        # Scale back up face locations since the frame we detected in was scaled to 1/4 size
+        top *= 4
+        right *= 4
+        bottom *= 4
+        left *= 4
+
+        # Draw a box around the face
+        cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)
+
+        # Draw a label with a name below the face
+        cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)
+        font = cv2.FONT_HERSHEY_DUPLEX
+        cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)
+
+    # Display the resulting image
+    cv2.imshow('Video', frame)
+
+    # Hit 'q' on the keyboard to quit!
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+
+# Release handle to the webcam
+video_capture.release()
+cv2.destroyAllWindows()