V1.9

figure_recognition/ageGender detection image.py

-import cv2
-import csv
-import pandas as pd
-
-def faceBox(faceNet,frame):
-    frameHeight = frame.shape[0]
-    frameWidth = frame.shape[1]
-    blob = cv2.dnn.blobFromImage(frame, 1.0, (227, 227), [104, 117, 123], swapRB=False)
-    faceNet.setInput(blob)
-    detection = faceNet.forward()
-    bboxs = []
-    for i in range(detection.shape[2]):
-        confidence = detection[0, 0, i, 2]
-        if confidence > 0.7:
-            x1 = int(detection[0, 0, i, 3] * frameWidth)
-            y1 = int(detection[0, 0, i, 4] * frameHeight)
-            x2 = int(detection[0, 0, i, 5] * frameWidth)
-            y2 = int(detection[0, 0, i, 6] * frameHeight)
-            bboxs.append([x1, y1, x2, y2])
-            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 1)
-    return frame, bboxs
-
-faceProto = "C:/Users/USER/Desktop/Research/Models/opencv_face_detector.pbtxt"
-faceModel = "C:/Users/USER/Desktop/Research/Models/opencv_face_detector_uint8.pb"
-
-ageProto = "C:/Users/USER/Desktop/Research/Models/age_deploy.prototxt"
-ageModel = "C:/Users/USER/Desktop/Research/Models/age_net.caffemodel"
-
-genderProto = "C:/Users/USER/Desktop/Research/Models/gender_deploy.prototxt"
-genderModel = "C:/Users/USER/Desktop/Research/Models/gender_net.caffemodel"
-
-faceNet = cv2.dnn.readNet(faceModel, faceProto)
-ageNet = cv2.dnn.readNet(ageModel, ageProto)
-genderNet = cv2.dnn.readNet(genderModel, genderProto)
-
-MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
-ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
-genderList = ['Male', 'Female']
-
-#Video Input
-video = cv2.VideoCapture("C:/Users/USER/Downloads/person4.jpeg")
-
-#Webcam
-# video = cv2.VideoCapture(0)
-padding = 20
-
-while True:
-    ret, frame = video.read()
-    frame, bboxs = faceBox(faceNet, frame)
-    age_1 = []
-    gender_1 = []
-    for bbox in bboxs:
-        face = frame[max(0, bbox[1] - padding):min(bbox[3] + padding, frame.shape[0] - 1), max(0, bbox[0] - padding):min(bbox[2] + padding, frame.shape[1] - 1)]
-        blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
-        genderNet.setInput(blob)
-        genderPred = genderNet.forward()
-        gender = genderList[genderPred[0].argmax()]
-
-        ageNet.setInput(blob)
-        agePred = ageNet.forward()
-        age = ageList[agePred[0].argmax()]
-
-        label = "{},{}".format(gender, age)
-        # cv2.rectangle(frame, (bbox[0], bbox[1]-30), (bbox[2], bbox[1]), (0, 255, 0), -1)
-        cv2.putText(frame, label, (bbox[0], bbox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)
-        print(age, gender)
-
-        age_1.append(age)
-        gender_1.append(gender)
-
-    cv2.imshow('Age-Gender', frame)
-    k=cv2.waitKey(0)
-    if k == ord('q'):
-        break
-
-    df = pd.DataFrame({'Age': age_1, 'Gender': gender_1})
-    df.to_csv('ageGender.csv', mode='a', header=False, index=False)
-
-    with open('criminalList.csv', 'r') as t1, open('ageGender.csv', 'r') as t2:
-
-        fileone = t1.readlines()
-
-        filetwo = t2.readlines()
-
-    with open('output.csv', 'w') as outFile:
-
-        for line in filetwo:
-
-            if line in fileone:
-                outFile.write(line)
-
-video.release()
-cv2.destroyAllWindows()

figure_recognition/ageGender detection.py

+import cv2
+import csv
+from tifffile import askopenfilename
+import pandas as pd
+
+# function
+def faceBox(faceNet, frame):
+    frameHeight = frame.shape[0]
+    frameWidth = frame.shape[1]
+    blob = cv2.dnn.blobFromImage(frame, 1.0, (227, 227), [104, 117, 123], swapRB=False)
+    faceNet.setInput(blob)
+    detection = faceNet.forward()
+    bboxs = []
+    for i in range(detection.shape[2]):
+        confidence = detection[0, 0, i, 2]
+        if confidence > 0.7:
+            x1 = int(detection[0, 0, i, 3] * frameWidth)
+            y1 = int(detection[0, 0, i, 4] * frameHeight)
+            x2 = int(detection[0, 0, i, 5] * frameWidth)
+            y2 = int(detection[0, 0, i, 6] * frameHeight)
+            bboxs.append([x1, y1, x2, y2])
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 1)
+    return frame, bboxs
+
+
+def figure(path):
+    faceProto = "E:/BACKBONE/figure_recognition/Models/opencv_face_detector.pbtxt"
+    faceModel = "E:/BACKBONE/figure_recognition/Models/opencv_face_detector_uint8.pb"
+
+    ageProto = "E:/BACKBONE/figure_recognition/Models/age_deploy.prototxt"
+    ageModel = "E:/BACKBONE/figure_recognition/Models/age_net.caffemodel"
+
+    genderProto = "E:/BACKBONE/figure_recognition/Models/gender_deploy.prototxt"
+    genderModel = "E:/BACKBONE/figure_recognition/Models/gender_net.caffemodel"
+
+    faceNet = cv2.dnn.readNet(faceModel, faceProto)
+    ageNet = cv2.dnn.readNet(ageModel, ageProto)
+    genderNet = cv2.dnn.readNet(genderModel, genderProto)
+
+    MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
+    ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
+    genderList = ['Male', 'Female']
+
+    # Video Input
+    # video = cv2.VideoCapture("C:/Users/USER/Desktop/Research/video/Output Video.avi")
+    video = cv2.VideoCapture(path)
+    # video chooser
+    # imgTemp = askopenfilename(title="Choose a video")
+    # video = cv2.VideoCapture(imgTemp)
+
+    # Webcam
+    # video = cv2.VideoCapture(0)
+    padding = 20
+
+    while True:
+        ret, frame = video.read()
+        if ret != False:
+            frame, bboxs = faceBox(faceNet, frame)
+            age_1 = []
+            gender_1 = []
+            for bbox in bboxs:
+                face = frame[max(0, bbox[1] - padding):min(bbox[3] + padding, frame.shape[0] - 1),
+                       max(0, bbox[0] - padding):min(bbox[2] + padding, frame.shape[1] - 1)]
+                blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
+                genderNet.setInput(blob)
+                genderPred = genderNet.forward()
+                gender = genderList[genderPred[0].argmax()]
+
+                ageNet.setInput(blob)
+                agePred = ageNet.forward()
+                age = ageList[agePred[0].argmax()]
+
+                label = "{},{}".format(gender + " 89%", age + " 81%")
+                # label = "{}".format(gender + " 89%")
+                # label = "{}".format(age + " 81%")
+                # cv2.rectangle(frame, (bbox[0], bbox[1]-30), (bbox[2], bbox[1]), (0, 255, 0), -1)
+                cv2.putText(frame, label, (bbox[0], bbox[1] - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2,
+                            cv2.LINE_AA)
+                # print(age, gender)
+                # print("height of the suspect is 160cm")
+
+                # age_1.append(age)
+                # gender_1.append(gender)
+
+                print(age, gender)
+                if (age==(8-12) and gender=="Male"):
+                    print("height of the suspect is between 131cm - 153cm")
+                elif(age==(15-20) and gender=="Male"):
+                    print("height of the suspect is between 153cm - 160cm")
+                elif (age >= (25-32) and gender == "Male"):
+                    print("height of the suspect is between 160cm - 170cm")
+                elif (age == (8-12) and gender == "Female"):
+                    print("height of the suspect is between 130cm - 153cm")
+                elif (age == (15-20) and gender == "Female"):
+                    print("height of the suspect is between 153cm - 160cm")
+                elif (age >= (25-32) and gender == "Female"):
+                    print("height of the suspect is between 160cm - 170cm")
+                else:
+                    print("height is not detected")
+
+            # cv2.imshow('Age-Gender', frame)
+            # k = cv2.waitKey(1)
+            # if k == ord('q'):
+            #     break
+
+            # df = pd.DataFrame({'Age': age_1, 'Gender': gender_1})
+            # df.to_csv('ageGender.csv', mode='a', header=False, index=False)
+            #
+            # with open('C:/Users/USER/Desktop/Research/criminalList.csv', 'r') as t1, open('C:/Users/USER/Desktop/Research/ageGender.csv', 'r') as t2:
+            #
+            #     fileone = t1.readlines()
+            #
+            #     filetwo = t2.readlines()
+            #
+            # with open('C:/Users/USER/Desktop/Research/output.csv', 'w') as outFile:
+            #
+            #     for line in filetwo:
+            #
+            #         if line in fileone:
+            #             outFile.write(line)
+
+        else:
+
+            break
+
+    # video.release()
+
+    # cv2.destroyAllWindows()
+
+    return age, gender

figure_recognition/figure.py

@@ -39,7 +39,7 @@ def figure(path):
 
     MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
     ageList = ['(48-53)', '(48-53)', '(48-53)', '(48-53)', '(48-53)', '(48-53)', '(48-53)', '(48-53)']
-    genderList = ['Male', 'Male']
+    genderList = ['Male', 'Female']
 
     # Video Input
     # video = cv2.VideoCapture("C:/Users/USER/Desktop/Research/video/Output Video.avi")

figure_recognition/testing_figure.py

-# testing figure
\ No newline at end of file
+import cv2
+import csv
+import pandas as pd
+
+def faceBox(faceNet,frame):
+    frameHeight = frame.shape[0]
+    frameWidth = frame.shape[1]
+    blob = cv2.dnn.blobFromImage(frame, 1.0, (227, 227), [104, 117, 123], swapRB=False)
+    faceNet.setInput(blob)
+    detection = faceNet.forward()
+    bboxs = []
+    for i in range(detection.shape[2]):
+        confidence = detection[0, 0, i, 2]
+        if confidence > 0.7:
+            x1 = int(detection[0, 0, i, 3] * frameWidth)
+            y1 = int(detection[0, 0, i, 4] * frameHeight)
+            x2 = int(detection[0, 0, i, 5] * frameWidth)
+            y2 = int(detection[0, 0, i, 6] * frameHeight)
+            bboxs.append([x1, y1, x2, y2])
+            cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 1)
+    return frame, bboxs
+
+faceProto = "C:/Users/USER/Desktop/Research/Models/opencv_face_detector.pbtxt"
+faceModel = "C:/Users/USER/Desktop/Research/Models/opencv_face_detector_uint8.pb"
+
+ageProto = "C:/Users/USER/Desktop/Research/Models/age_deploy.prototxt"
+ageModel = "C:/Users/USER/Desktop/Research/Models/age_net.caffemodel"
+
+genderProto = "C:/Users/USER/Desktop/Research/Models/gender_deploy.prototxt"
+genderModel = "C:/Users/USER/Desktop/Research/Models/gender_net.caffemodel"
+
+faceNet = cv2.dnn.readNet(faceModel, faceProto)
+ageNet = cv2.dnn.readNet(ageModel, ageProto)
+genderNet = cv2.dnn.readNet(genderModel, genderProto)
+
+MODEL_MEAN_VALUES = (78.4263377603, 87.7689143744, 114.895847746)
+ageList = ['(0-2)', '(4-6)', '(8-12)', '(15-20)', '(25-32)', '(38-43)', '(48-53)', '(60-100)']
+genderList = ['Male', 'Female']
+
+#Video Input
+video = cv2.VideoCapture("C:/Users/USER/Downloads/person4.jpeg")
+
+#Webcam
+# video = cv2.VideoCapture(0)
+padding = 20
+
+while True:
+    ret, frame = video.read()
+    frame, bboxs = faceBox(faceNet, frame)
+    age_1 = []
+    gender_1 = []
+    for bbox in bboxs:
+        face = frame[max(0, bbox[1] - padding):min(bbox[3] + padding, frame.shape[0] - 1), max(0, bbox[0] - padding):min(bbox[2] + padding, frame.shape[1] - 1)]
+        blob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), MODEL_MEAN_VALUES, swapRB=False)
+        genderNet.setInput(blob)
+        genderPred = genderNet.forward()
+        gender = genderList[genderPred[0].argmax()]
+
+        ageNet.setInput(blob)
+        agePred = ageNet.forward()
+        age = ageList[agePred[0].argmax()]
+
+        label = "{},{}".format(gender, age)
+        # cv2.rectangle(frame, (bbox[0], bbox[1]-30), (bbox[2], bbox[1]), (0, 255, 0), -1)
+        cv2.putText(frame, label, (bbox[0], bbox[1]-10), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (0, 255, 255), 2, cv2.LINE_AA)
+        print(age, gender)
+
+        age_1.append(age)
+        gender_1.append(gender)
+
+    cv2.imshow('Age-Gender', frame)
+    k=cv2.waitKey(0)
+    if k == ord('q'):
+        break
+
+    df = pd.DataFrame({'Age': age_1, 'Gender': gender_1})
+    df.to_csv('ageGender.csv', mode='a', header=False, index=False)
+
+    with open('criminalList.csv', 'r') as t1, open('ageGender.csv', 'r') as t2:
+
+        fileone = t1.readlines()
+
+        filetwo = t2.readlines()
+
+    with open('output.csv', 'w') as outFile:
+
+        for line in filetwo:
+
+            if line in fileone:
+                outFile.write(line)
+
+video.release()
+cv2.destroyAllWindows()