Merge pull request #11 from pitfisher/fps_visualization

Fps visualization

Author: automaticdai

src/camera-test/cv_camera_test.py

@@ -11,27 +11,64 @@
 # ------------------------------------------------------------------------------
 
 import cv2
+import time
+import numpy as np
 
 CAMERA_DEVICE_ID = 0
 IMAGE_WIDTH = 320
 IMAGE_HEIGHT = 240
+fps = 0
+
+
+def visualize_fps(image, fps: int):
+    if len(np.shape(image)) < 3:
+        text_color = (255, 255, 255)  # white
+    else:
+        text_color = (0, 255, 0)  # green
+    row_size = 20  # pixels
+    left_margin = 24  # pixels
+
+    font_size = 1
+    font_thickness = 1
+
+    # Draw the FPS counter
+    fps_text = 'FPS = {:.1f}'.format(fps)
+    text_location = (left_margin, row_size)
+    cv2.putText(image, fps_text, text_location, cv2.FONT_HERSHEY_PLAIN,
+                font_size, text_color, font_thickness)
+
+    return image
+
 
 if __name__ == "__main__":
     try:
         # create video capture
         cap = cv2.VideoCapture(CAMERA_DEVICE_ID)
 
-        # set resolution to 320x240 to reduce latency 
+        # set resolution to 320x240 to reduce latency
         cap.set(3, IMAGE_WIDTH)
         cap.set(4, IMAGE_HEIGHT)
 
         # Loop to continuously get images
         while True:
+            # ----------------------------------------------------------------------
+            # record start time
+            start_time = time.time()
+
             # Read the frames from a camera
             _, frame = cap.read()
 
             # show image
-            cv2.imshow('frame', frame)
+            cv2.imshow('frame', visualize_fps(frame, fps))
+
+            # ----------------------------------------------------------------------
+            # record end time
+            end_time = time.time()
+
+            # calculate FPS
+            seconds = end_time - start_time
+            fps = 1.0 / seconds
+            print("Estimated fps:{0:0.1f}".format(fps))
 
             # if key pressed is 'Esc' then exit the loop
             if cv2.waitKey(33) == 27:

src/face-detection/face-detection.py

@@ -1,4 +1,6 @@
 import cv2
+import numpy as np
+import time
 
 # ------------------------------------------------------------------------------
 # automaticdai
@@ -7,19 +9,45 @@
 # Twitter: @yfrobotics
 # Website: https://www.yfrl.org
 # ------------------------------------------------------------------------------
-# Reference: 
+# Reference:
 # - https://towardsdatascience.com/face-detection-in-2-minutes-using-opencv-python-90f89d7c0f81
 # ------------------------------------------------------------------------------
 
+fps = 0
+
+
+def visualize_fps(image, fps: int):
+    if len(np.shape(image)) < 3:
+        text_color = (255, 255, 255)  # white
+    else:
+        text_color = (0, 255, 0)  # green
+    row_size = 20  # pixels
+    left_margin = 24  # pixels
+
+    font_size = 1
+    font_thickness = 1
+
+    # Draw the FPS counter
+    fps_text = 'FPS = {:.1f}'.format(fps)
+    text_location = (left_margin, row_size)
+    cv2.putText(image, fps_text, text_location, cv2.FONT_HERSHEY_PLAIN,
+                font_size, text_color, font_thickness)
+
+    return image
+
+
 # Load the cascade
 face_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')
 
-# To capture video from webcam. 
+# To capture video from webcam.
 cap = cv2.VideoCapture(0)
-# To use a video file as input 
+# To use a video file as input
 # cap = cv2.VideoCapture('filename.mp4')
 
 while True:
+    # ----------------------------------------------------------------------
+    # record start time
+    start_time = time.time()
     # Read the frame
     _, img = cap.read()
     # Convert to grayscale
@@ -30,7 +58,14 @@
     for (x, y, w, h) in faces:
         cv2.rectangle(img, (x, y), (x+w, y+h), (255, 0, 0), 2)
     # Display
-    cv2.imshow('img', img)
+    cv2.imshow('img', visualize_fps(img, fps))
+    # ----------------------------------------------------------------------
+    # record end time
+    end_time = time.time()
+    # calculate FPS
+    seconds = end_time - start_time
+    fps = 1.0 / seconds
+    print("Estimated fps:{0:0.1f}".format(fps))
     # Stop if escape key is pressed
     k = cv2.waitKey(30) & 0xff
     if k==27:

src/motion-detection/cv_motion_detection.py

@@ -20,6 +20,7 @@
 MOTION_BLUR = True
 
 cnt_frame = 0
+fps = 0
 
 
 def mse(image_a, image_b):
@@ -34,6 +35,26 @@ def mse(image_a, image_b):
     return err
 
 
+def visualize_fps(image, fps: int):
+    if len(np.shape(image)) < 3:
+        text_color = (255, 255, 255)  # white
+    else:
+        text_color = (0, 255, 0)  # green
+    row_size = 20  # pixels
+    left_margin = 24  # pixels
+
+    font_size = 1
+    font_thickness = 1
+
+    # Draw the FPS counter
+    fps_text = 'FPS = {:.1f}'.format(fps)
+    text_location = (left_margin, row_size)
+    cv2.putText(image, fps_text, text_location, cv2.FONT_HERSHEY_PLAIN,
+                font_size, text_color, font_thickness)
+
+    return image
+
+
 if __name__ == "__main__":
     try:
         # create video capture
@@ -64,8 +85,8 @@ def mse(image_a, image_b):
             edges = cv2.Canny(frame_gray,100,200)
 
             # Show the original and processed image
-            cv2.imshow('gray', frame_gray)
-            cv2.imshow('edge', edges)
+            cv2.imshow('gray', visualize_fps(frame_gray, fps))
+            cv2.imshow('edge', visualize_fps(edges, fps))
 
             # Calculate MSE
             if cnt_frame > 0:

src/object-tracking-color/cv_object_tracking_color.py

@@ -20,16 +20,19 @@
 
 import cv2
 import numpy as np
+import time
 
 CAMERA_DEVICE_ID = 0
 IMAGE_WIDTH = 320
 IMAGE_HEIGHT = 240
+fps = 0
 
 hsv_min = np.array((50, 80, 80))
 hsv_max = np.array((120, 255, 255))
 
 colors = []
 
+
 def isset(v):
     try:
         type (eval(v))
@@ -109,6 +112,26 @@ def rgb2hsv(r, g, b):
     return (h, s, v)
 
 
+def visualize_fps(image, fps: int):
+    if len(np.shape(image)) < 3:
+        text_color = (255, 255, 255)  # white
+    else:
+        text_color = (0, 255, 0)  # green
+    row_size = 20  # pixels
+    left_margin = 24  # pixels
+
+    font_size = 1
+    font_thickness = 1
+
+    # Draw the FPS counter
+    fps_text = 'FPS = {:.1f}'.format(fps)
+    text_location = (left_margin, row_size)
+    cv2.putText(image, fps_text, text_location, cv2.FONT_HERSHEY_PLAIN,
+                font_size, text_color, font_thickness)
+
+    return image
+
+
 if __name__ == "__main__":
     try:
         # create video capture
@@ -119,6 +142,9 @@ def rgb2hsv(r, g, b):
         cap.set(4, IMAGE_HEIGHT)
 
         while True:
+            # ----------------------------------------------------------------------
+            # record start time
+            start_time = time.time()
             # Read the frames frome a camera
             _, frame = cap.read()
             frame = cv2.blur(frame,(3,3))
@@ -128,6 +154,7 @@ def rgb2hsv(r, g, b):
 
             # Convert the image to hsv space and find range of colors
             hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)
+            cv2.namedWindow('frame')
             cv2.setMouseCallback('frame', on_mouse_click, frame)
 
             # Uncomment this for RED tag
@@ -179,9 +206,15 @@ def rgb2hsv(r, g, b):
 
             # Show the original and processed image
             #res = cv2.bitwise_and(frame, frame, mask=thresh2)
-            cv2.imshow('frame', frame)
-            cv2.imshow('thresh', thresh2)
-
+            cv2.imshow('frame', visualize_fps(frame, fps))
+            cv2.imshow('thresh', visualize_fps(thresh2, fps))
+            # ----------------------------------------------------------------------
+            # record end time
+            end_time = time.time()
+            # calculate FPS
+            seconds = end_time - start_time
+            fps = 1.0 / seconds
+            print("Estimated fps:{0:0.1f}".format(fps));
             # if key pressed is 'Esc' then exit the loop
             if cv2.waitKey(33) == 27:
                 break

src/object-tracking-feature/orb.py

@@ -14,7 +14,7 @@
 
 import cv2
 import numpy as np
-
+import time
 
 def ORB_detector(new_image, image_template):
     # Function that compares input image to template
@@ -41,12 +41,38 @@ def ORB_detector(new_image, image_template):
     matches = sorted(matches, key=lambda val: val.distance)
     return len(matches)
 
+
+def visualize_fps(image, fps: int):
+    if len(np.shape(image)) < 3:
+        text_color = (255, 255, 255)  # white
+    else:
+        text_color = (0, 255, 0)  # green
+    row_size = 20  # pixels
+    left_margin = 24  # pixels
+
+    font_size = 1
+    font_thickness = 1
+
+    # Draw the FPS counter
+    fps_text = 'FPS = {:.1f}'.format(fps)
+    text_location = (left_margin, row_size)
+    cv2.putText(image, fps_text, text_location, cv2.FONT_HERSHEY_PLAIN,
+                font_size, text_color, font_thickness)
+
+    return image
+
+
+fps = 0
+
 cap = cv2.VideoCapture(0)
 
 # Load our image template, this is our reference image
 image_template = cv2.imread('simple.png', 0) 
 
 while True:
+    # ----------------------------------------------------------------------
+    # record start time
+    start_time = time.time()
     # Get webcam images
     ret, frame = cap.read()
 
@@ -85,7 +111,14 @@ def ORB_detector(new_image, image_template):
         cv2.rectangle(frame, (top_left_x,top_left_y), (bottom_right_x,bottom_right_y), (0,255,0), 3)
         cv2.putText(frame,'Object Found',(50,50), cv2.FONT_HERSHEY_COMPLEX, 2 ,(0,255,0), 2)
 
-    cv2.imshow('Object Detector using ORB', frame)
+    cv2.imshow('Object Detector using ORB', visualize_fps(frame, fps))
+    # ----------------------------------------------------------------------
+    # record end time
+    end_time = time.time()
+    # calculate FPS
+    seconds = end_time - start_time
+    fps = 1.0 / seconds
+    print("Estimated fps:{0:0.1f}".format(fps))
     if cv2.waitKey(1) == 13: #13 is the Enter Key
         break