simple object detection

main.py

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+# Python program to implement
+# Webcam Motion Detector
+
+# importing OpenCV, time and Pandas library
+import cv2, time, pandas
+# importing datetime class from datetime library
+from datetime import datetime
+
+# Assigning our static_back to None
+static_back = None
+
+# List when any moving object appear
+motion_list = [ None, None ]
+
+# Time of movement
+time = []
+
+# Initializing DataFrame, one column is start
+# time and other column is end time
+df = pandas.DataFrame(columns = ["Start", "End"])
+
+# Capturing video
+video = cv2.VideoCapture(0)
+
+# Infinite while loop to treat stack of image as video
+while True:
+    # Reading frame(image) from video
+    check, frame = video.read()
+
+    # Initializing motion = 0(no motion)
+    motion = 0
+
+    # Converting color image to gray_scale image
+    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
+
+    # Converting gray scale image to GaussianBlur
+    # so that change can be find easily
+    gray = cv2.GaussianBlur(gray, (21, 21), 0)
+
+    # In first iteration we assign the value
+    # of static_back to our first frame
+    if static_back is None:
+        static_back = gray
+        continue
+
+    # Difference between static background
+    # and current frame(which is GaussianBlur)
+    diff_frame = cv2.absdiff(static_back, gray)
+
+    # If change in between static background and
+    # current frame is greater than 30 it will show white color(255)
+    thresh_frame = cv2.threshold(diff_frame, 30, 255, cv2.THRESH_BINARY)[1]
+    thresh_frame = cv2.dilate(thresh_frame, None, iterations = 2)
+
+    # Finding contour of moving object
+    cnts,_ = cv2.findContours(thresh_frame.copy(),
+                       cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    for contour in cnts:
+        if cv2.contourArea(contour) < 10000:
+            continue
+        motion = 1
+
+        (x, y, w, h) = cv2.boundingRect(contour)
+        # making green rectangle around the moving object
+        cv2.rectangle(frame, (x, y), (x + w, y + h), (0, 255, 0), 3)
+
+    # Appending status of motion
+    motion_list.append(motion)
+
+    motion_list = motion_list[-2:]
+
+    # Appending Start time of motion
+    if motion_list[-1] == 1 and motion_list[-2] == 0:
+        time.append(datetime.now())
+
+    # Appending End time of motion
+    if motion_list[-1] == 0 and motion_list[-2] == 1:
+        time.append(datetime.now())
+
+    # Displaying image in gray_scale
+    cv2.imshow("Gray Frame", gray)
+
+    # Displaying the difference in currentframe to
+    # the staticframe(very first_frame)
+    cv2.imshow("Difference Frame", diff_frame)
+
+    # Displaying the black and white image in which if
+    # intensity difference greater than 30 it will appear white
+    cv2.imshow("Threshold Frame", thresh_frame)
+
+    # Displaying color frame with contour of motion of object
+    cv2.imshow("Color Frame", frame)
+
+    key = cv2.waitKey(1)
+    # if q entered whole process will stop
+    if key == ord('q'):
+        # if something is movingthen it append the end time of movement
+        if motion == 1:
+            time.append(datetime.now())
+        break
+
+# Appending time of motion in DataFrame
+for i in range(0, len(time), 2):
+    df = df.append({"Start":time[i], "End":time[i + 1]}, ignore_index = True)
+
+# Creating a CSV file in which time of movements will be saved
+df.to_csv("Time_of_movements.csv")
+
+video.release()
+
+# Destroying all the windows
+cv2.destroyAllWindows()