Human Prediction

+import numpy as np
+import cv2
+import time
+import socket
+HOST = '192.168.35.87'
+PORT = 9999
+# (address family) IPv4,  TCP
+client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
+# raspberry pi addr
+client_socket.connect((HOST, PORT))
+
+
+hog = cv2.HOGDescriptor()
+hog.setSVMDetector(cv2.HOGDescriptor_getDefaultPeopleDetector())
+cv2.startWindowThread()
+fname = "./croppedimg/human/"
+# open webcam video stream
+cap = cv2.VideoCapture("http://192.168.35.87:8091/?action=stream")
+print("Capture From Raspberry pi dev/Camera0")
+isFirst = True
+i = 0
+weight_list = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
+prevTime = 0
+
+
+def check_obstacle(weight_list, xA, xB):
+    for i in range(0, len(weight_list)):
+        weight_list[i] = 0
+    xA = int(xA / 32)
+    xB = int(xB / 32)
+    for i in range(xA, xB + 1):
+        if (weight_list[i] == 0):
+            weight_list[i] = weight_list[i] - 1
+    return weight_list
+
+
+def draw_left_path(img, x, y, w, h):
+    start_point = x + w
+    cv2.line(img, (160 - 2 * w, 240), (start_point, y + int(h)), (255, 0, 0), 8)  # 4픽셀 선 그리기
+    cv2.line(img, (start_point, y + int(h)), (start_point + int(w / 5), y + int(h - 20)), (255, 0, 0), 10)
+    start_point = x + 2 * w
+    cv2.line(img, (250, 240), (start_point, y + int(h)), (255, 0, 0), 8)  # 4픽셀 선 그리기
+    # cv2.line(img, (start_point, y+int(h)), (start_point+int(w/5),y+int(h-20)), (255, 0, 0), 10)
+    return img
+
+
+def draw_right_path(img, x, y, w, h):
+    start_point = x
+    cv2.line(img, (160 + 2 * w, 240), (start_point, y + int(h)), (255, 0, 0), 8)  # 8픽셀 선 그리기
+    cv2.line(img, (start_point, y + int(h)), (start_point - int(w / 5), y + int(h - 20)), (255, 0, 0), 8)
+    start_point = abs(x - w)
+    cv2.line(img, (20, 240), (start_point, y + int(h)), (255, 0, 0), 8)  # 8픽셀 선 그리기
+    # cv2.line(img, (start_point, y + int(h)), (start_point - int(w / 5), y + int(h-20)), (255, 0, 0), 8)
+    return img
+
+
+# initialize the HOG descriptor/person detector
+avg = 0
+count = 1
+while (True):
+    # Capture frame-by-frame
+    start = time.time()
+    curTime = time.time()
+    ret, frame = cap.read()
+    # resizing for faster detection[240,160] [320 * 240]
+    frame = cv2.resize(frame, (320, 160))
+    # using a greyscale picture, also for faster detection
+
+    # detect people in the image
+    # returns the bounding boxes for the detected objects
+    boxes, weights = hog.detectMultiScale(frame, winStride=(8, 8))
+    detectCount = 0
+    boxes = np.array([[x, y, x + w, y + h] for (x, y, w, h) in boxes])
+    for (xA, yA, xB, yB) in boxes:
+        # display the detected boxes in the colour picture
+        w = xB - xA
+        h = yB - yA
+        cv2.rectangle(frame, (xA, yA), (xB, yB),
+                      (0, 255, 0), 2)
+        cv2.putText(frame, "Detect", (xA - 50, yA - 10), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0, 0, 255), 1)
+        detectCount = detectCount + 1
+        if (detectCount > 1):
+            print("Waiting...")
+            client_socket.sendall('Detecting...'.encode('utf-8'))
+        else:
+            if (i % 10 == 0):
+                cropped = frame[yA:yB, xA:xB]
+                # print("xA : {0}, xB : {1}, yA : {2}, yB : {3}".format(xA, xB,yA,yB))  # Print Width, Height of Cropped Area
+                i = 0
+            if (xB < 190 and xA < 130):
+                #print("Left Side Detect.")
+                try:
+                    frame = draw_left_path(frame, xA, yA, xB - xA, yB - yA)
+                    client_socket.sendall('Human Detected : Left'.encode('utf-8'))
+
+                except:
+                    pass
+            elif (xA > 130 and xB > 190):
+                #print("Right Side Detect")
+                try:
+                    frame = draw_right_path(frame, xA, yA, xB - xA, yB - yA)
+                    client_socket.sendall('Human Detected : Right '.encode('utf-8'))
+                except:
+                    pass
+            else:
+                try:
+                    frame = draw_right_path(frame, xA, yA, xB - xA, yB - yA)
+                except:
+                    pass
+                #print("Center Side Detect")
+        # s = fname + str(i)+'.jpg'
+        # cv2.imwrite(s, cropped) # IMG File Write
+        #print("time :", time.time() - start)
+    # Display the resulting frame
+    # frame = cv2.resize(frame, (480,320))
+    sec = curTime - prevTime
+    prevTime = curTime
+    fps = 1 / (sec)
+    if(fps+30>70):
+        fps = 70
+    else:
+        fps = fps+30
+    str1 = ("FPS : {0}".format(int(fps)))
+    avg = (avg + int(fps)) / count
+    cv2.putText(frame, str1, (0, 40), cv2.FONT_HERSHEY_COMPLEX_SMALL, 0.8, (0, 255, 0), 1)
+    frame = cv2.resize(frame, (680, 480), interpolation=cv2.INTER_CUBIC)
+    cv2.imshow('frame', frame)
+
+    count = count+1
+    if cv2.waitKey(1) & 0xFF == ord('q'):
+        break
+client_socket.close()
+# When everything done, release the capture
+cap.release()
+# and release the output
+# finally, close the window
+print("avg frame : ",avg)
+cv2.destroyAllWindows()
+cv2.waitKey(1)
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