Max Score 500 with Data Extraction from Image , Need Better Frame Rat…

…es for Better Performance

.gitignore

@@ -0,0 +1,7 @@
+# Don't track content of these folders
+screens/  
+Logistic_Regression_with_image/1/
+Extract_info_Image/1/
+CNN_with_image/1/
+Logistic_Regression_Data/1/
+temp/

Extract_info_Image/Feature_Extractor.py

@@ -0,0 +1,66 @@
+import csv
+from PIL import Image
+import numpy as np
+import PIL.ImageOps    
+import cv2
+from selenium import webdriver
+from selenium.webdriver.common.keys import Keys
+
+def mouse_callback(event, x, y, flags, params):
+    global i
+    print y , x , i[y,x]
+
+
+
+files = []
+action = []
+
+cactusBase = 158
+MIN_LENGTH = 80
+
+with open("1/out.csv" , "r") as f :
+    data = csv.reader(f, delimiter=',')
+    for i in data:
+        if(i[0] == 1):
+            action.append(0)
+        else:
+            action.append(1)
+        files.append(i[1])
+
+
+def parse_function(filename,basewidth = 0):
+    img = img = cv2.imread(filename,cv2.IMREAD_GRAYSCALE)
+    #img = PIL.ImageOps.invert(img)
+    #wpercent = (basewidth/float(img.size[0]))
+    #hsize = int((float(img.size[1])*float(wpercent)))
+    #img = img.resize((basewidth,hsize), Image.ANTIALIAS)    
+    return img
+
+def length_to_cactus(imgline):
+    startpos = 1000
+    i = 0
+    black = False
+    inter = True
+    while i < len(imgline):
+        if(imgline[i] == 0 and black == True):
+            if(i - startpos < MIN_LENGTH):black = False            
+            else:return str(startpos) + "   " +str( i - startpos)
+        if(imgline[i] == 0 and black == False):
+            if(inter):
+                startpos = i
+            inter = False
+            black=True
+        i+=1 
+    return str(startpos) +"   " + "1000"
+
+x_v = [np.array(parse_function(i))  for i in files ] 
+y_v = action 
+font = cv2.FONT_HERSHEY_SIMPLEX
+
+for i in x_v:
+    cv2.namedWindow('image', cv2.WINDOW_NORMAL)
+    cv2.resizeWindow('image', 1920, 500)
+    cv2.putText(i, str(length_to_cactus(i[cactusBase] )) , (5, 15), cv2.FONT_HERSHEY_PLAIN , 0.9, (155, 155, 155))     
+    cv2.imshow("image", i)
+    cv2.setMouseCallback('image', mouse_callback)
+    cv2.waitKey(0)

Extract_info_Image/helper.py

@@ -0,0 +1,34 @@
+
+import numpy as np
+
+from PIL import Image
+import keyboard
+import cv2
+
+def mouse_callback(event, x, y, flags, params):
+    global thresh_img
+    print x , y , thresh_img[y,x]
+
+
+img = cv2.imread('1/train-122.png',0)
+
+
+
+ret,thresh_img = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
+
+#cv2.imwrite('bw_image.png', thresh_img)
+
+cv2.namedWindow('image', cv2.WINDOW_NORMAL)
+cv2.resizeWindow('image', 1920, 500)
+
+
+cv2.setMouseCallback('image', mouse_callback)
+
+
+
+
+
+cv2.imshow("image" , thresh_img)
+
+
+cv2.waitKey(0)

Logistic_Regression_Data/model.py

@@ -0,0 +1,154 @@
+import csv
+from PIL import Image
+import numpy as np
+import PIL.ImageOps    
+import cv2
+from selenium import webdriver
+from selenium.webdriver.common.keys import Keys
+
+import tensorflow as tf
+
+
+def mouse_callback(event, x, y, flags, params):
+    global i
+    print y , x , i[y,x]
+
+
+
+files = []
+action = []
+
+cactusBase = 156
+MIN_LENGTH = 100
+
+with open("1/out.csv" , "r") as f :
+    data = csv.reader(f, delimiter=',')
+    for i in data:
+        if(i[0] == 1):
+            action.append(0)
+        else:
+            action.append(1)
+        files.append(i[1])
+
+
+def parse_function(filename,basewidth = 0):
+    img = img = cv2.imread(filename,cv2.IMREAD_GRAYSCALE)
+    #img = PIL.ImageOps.invert(img)
+    #wpercent = (basewidth/float(img.size[0]))
+    #hsize = int((float(img.size[1])*float(wpercent)))
+    #img = img.resize((basewidth,hsize), Image.ANTIALIAS)    
+    return img
+
+def length_to_cactus(imgline):
+    startpos = 1000
+    i = 0
+    black = False
+    inter = True
+    while i < len(imgline):
+        if(imgline[i] == 0 and black == True):
+            if(i - startpos < MIN_LENGTH):black = False            
+            else:return startpos ,( i - startpos)
+        if(imgline[i] == 0 and black == False):
+            if(inter):
+                startpos = i
+            inter = False
+            black=True
+        i+=1 
+    return startpos  , 1000
+
+x_v = [ length_to_cactus(np.array(parse_function(i))[cactusBase])  for i in files ] 
+y_v = action 
+
+print "Loaded all Images"
+
+print "Starting Training NN ( Tensorflow ) " 
+
+'''
+Using a Neural Network with No Hidden Layers with 55*200 input layers. total of 55 * 200 Values.
+
+The weight dimentions are (55 * 200) * 1 , input is 1 *  ( 55 * 200 )
+
+Output is single neuron ( 1 - Neuron Fired -- Jump ) else ( Leave )
+
+'''
+
+
+# Parameters
+#learning_rate = .00000000001
+learning_rate =  .00000000001
+training_epochs = 50000
+batch_size = x_v.__len__()
+display_step = 110
+
+tf.set_random_seed(777)  
+x = tf.placeholder(tf.float32, [None, 2])
+y = tf.placeholder(tf.float32, [None]) 
+
+W = tf.Variable(tf.zeros([2, 1]))
+b = tf.Variable(tf.random_normal([1]))
+
+pred = tf.nn.tanh(tf.add(tf.matmul(x, W) , b) ) 
+
+
+#cost = tf.reduce_mean(-1 * (tf.reduce_sum(y*tf.log(pred) + tf.reduce_sum( (y-1)*tf.log(pred -1) ) )))
+cost = tf.reduce_mean(-1 * (tf.reduce_sum(y*tf.log(pred   ))  ) ) 
+
+optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(cost)
+
+
+correct_prediction = tf.equal(tf.cast(tf.argmax(pred, 1),"float64"),tf.cast(y , "float64") )
+accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float32"))
+
+init = tf.global_variables_initializer()
+
+with tf.Session() as sess:
+    sess.run(init)
+
+    for epoch in range(training_epochs):
+        epoch_loss = 0
+        for i in range(int(len(files)//batch_size)):
+            epoch_x, epoch_y = x_v[ (i * batch_size) : (i +1 ) * batch_size ] , y_v[ (i * batch_size) : (i +1 ) * batch_size ] 
+            #print sess.run(max_pool1, feed_dict={x_inp: epoch_x, y: epoch_y}).shape
+            _, c , w = sess.run([optimizer, cost, W], feed_dict={x: x_v,y: y_v})
+            #print sum(w) , c
+            epoch_loss += c
+
+        #print sum(w)
+        if (epoch+1) % display_step == 0:
+            print("Epoch:", '%04d' % (epoch+1), "cost=", "{}".format(epoch_loss))
+
+    print(sess.run([accuracy , pred] ,feed_dict={x: x_v,y: y_v }))
+
+
+    # Start Selenium Program
+
+    import math
+
+    driver = webdriver.Chrome()
+
+    driver.get("chrome://dino")
+    elem = driver.find_element_by_id("t")
+
+    variable = 1
+    while True:
+        if( elem.get_attribute("class") == "offline" ):continue
+        if(driver.execute_script("return Runner.instance_.playing;") == False ):
+            elem.send_keys(Keys.SPACE)
+            continue
+
+        driver.save_screenshot("temp/screenshot-{}.png".format(variable))
+        img = cv2.imread("temp/screenshot-{}.png".format( variable))
+
+        img = img[ 168 : 400 , 105: ]
+
+        ret,thresh_img = cv2.threshold(img,127,255,cv2.THRESH_BINARY)
+        cv2.imwrite("temp/train-1.png", thresh_img)
+
+        value =length_to_cactus(np.array(parse_function("temp/train-1.png"))[cactusBase] ) 
+
+        value = sess.run(pred , feed_dict={x : [value]})
+
+        print value
+
+        if(int(value) != 1) :
+            elem.send_keys(Keys.SPACE)

Logistic_Regression_with_image/file.csv

@@ -0,0 +1,5 @@
+col1,col2
+1,5
+2,6
+3,7
+4,8