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BlobDetection.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec  4 00:39:04 2018
+
+@author: amine bahlouli
+"""
+
+import cv2
+import numpy as np
+
+img = cv2.imread("Sunflowers.jpg",0)
+params = cv2.SimpleBlobDetector_Params()
+params.filterByArea = True
+params.filterByCircularity = True
+params.minCircularity = 0.00001
+params.minConvexity = 0.95
+params.filterByConvexity = True
+detector = cv2.SimpleBlobDetector_create(params)
+
+keypoints = detector.detect(img)
+
+blank=np.zeros((1,1))
+
+
+blob = cv2.drawKeypoints(img,keypoints,blank,(0,255,255),cv2.DRAW_MATCHES_FLAGS_DEFAULT)
+
+
+cv2.imshow("blob", blob)
+cv2.waitKey(0)
+cv2.destroyAllWindows()

circles_detection.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Dec  4 01:58:42 2018
+
+@author: amine bahlouli
+"""
+
+import cv2
+import numpy as np
+
+image = cv2.imread("blobs.jpg")
+
+cv2.imshow(",",image)
+cv2.waitKey(0)
+
+params = cv2.SimpleBlobDetector_Params()
+#params.filterByArea = True
+#params.filterByCircularity = True
+#params.minCircularity = 0.00001
+#params.minConvexity = 0.95
+#params.filterByConvexity = True
+detector = cv2.SimpleBlobDetector_create(params)
+keypoints = detector.detect(image)
+
+blank=np.zeros((1,1))
+blob = cv2.drawKeypoints(image,keypoints,blank,(0,255,255),cv2.DRAW_MATCHES_FLAGS_DEFAULT)
+number_of_blobs = len(keypoints)
+text= "total number of blobs "+str(len(keypoints))
+cv2.putText(blob,text,(20,550),cv2.FONT_HERSHEY_SIMPLEX,1,(100,0,255),2)
+
+cv2.imshow("blobs using default paramers",blob)
+cv2.waitKey(0)
+
+
+params = cv2.SimpleBlobDetector_Params()
+params.filterByArea = True
+params.filterByCircularity = True
+params.minCircularity = 0.9
+
+params.filterByConvexity = True
+params.minConvexity = 0.2
+params.filterByInertia=True
+params.minInertiaRatio=0.01
+detector = cv2.SimpleBlobDetector_create(params)
+keypoints = detector.detect(image)
+
+blank=np.zeros((1,1))
+blob = cv2.drawKeypoints(image,keypoints,blank,(0,255,255),cv2.DRAW_MATCHES_FLAGS_DEFAULT)
+number_of_blobs = len(keypoints)
+text= "total number of blobs "+str(len(keypoints))
+cv2.putText(blob,text,(20,550),cv2.FONT_HERSHEY_SIMPLEX,1,(100,0,255),2)
+
+cv2.imshow("blobs using default paramers",blob)
+cv2.waitKey(0)

convex_hull_contour.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Fri Nov 30 03:25:48 2018
+
+@author: amine bahlouli
+"""
+
+import numpy as np
+import cv2
+
+image = cv2.imread("hand.jpg")
+gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
+cv2.imshow("original image",image)
+cv2.waitKey(0)
+
+#threshhold image
+
+ret, thresh = cv2.threshold(gray,176,255,0)
+cv2.imshow("original image",thresh)
+cv2.waitKey(0)
+
+#find contours
+_,contours, hierarchy = cv2.findContours(thresh.copy(),cv2.RETR_LIST,cv2.CHAIN_APPROX_NONE)
+
+
+#sort contours by area and remove the largest frame contour
+n = len(contours)-1
+
+contours = sorted(contours, key=cv2.contourArea, reverse=False)[:n]
+
+for c in contours:
+    hull = cv2.convexHull(c)
+    cv2.drawContours(image,[hull],0,(0,255,0),2)
+    cv2.imshow("convex hull ", image)
+
+cv2.waitKey(0)

counting_contours_in_image.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Tue Nov 27 02:50:11 2018
+
+@author: amine bahlouli
+"""
+
+import cv2
+import numpy as np
+
+def x_cord_contour(contours):
+    # returns the x cordinate  for the contour centroid
+    if cv2.contourArea(contours)>10:
+        M = cv2.moments(contours)
+        return (int(M["m10"]/M["m00"]))
+
+def label_contour_center(image,c):
+    # places a red circle on the centers of contours
+    M = cv2.moments(c)
+    cx = (int(M["m10"]/M["m00"]))
+    cy = (int(M["m01"]/M["m00"]))
+
+    # draw circles in 
+    cv2.circle(image,(cx,cy),10,(0,0,255),-1)
+    return image
+
+image = cv2.imread("shapes.jpg")
+original_image = image.copy()
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+#canny edge
+edged = cv2.Canny(gray, 50,200)
+cv2.imshow("canny edge",edged)
+cv2.waitKey(0)
+_,contours, hierarchy = cv2.findContours(edged.copy(), cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_NONE)
+
+for (i,c) in enumerate(contours):
+    orig = label_contour_center(image,c)
+
+cv2.imshow("4 contour = centers", image)
+cv2.waitKey(0)
+
+contours_left_to_right = sorted(contours, key=x_cord_contour,reverse=False)
+
+for (i,c) in enumerate(contours_left_to_right):
+    cv2.drawContours(original_image,[c], -1,(0,0,255),-3)
+    M = cv2.moments(c)
+    cx = (int(M["m10"]/M["m00"]))
+    cy = (int(M["m01"]/M["m00"]))
+    cv2.putText(original_image,str(i+1),(cx,cy),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0),2)
+    cv2.imshow("left_to_rate", original_image)
+    cv2.waitKey(0)
+
+    (x,y,w,h) = cv2.boundingRect(c)
+
+    cropped_countour = original_image[y:y+h, x:x+w]
+    image_name = "output_number "+str(i+1)+".jpg"
+    cv2.imwrite(image_name,cropped_countour)
+cv2.destroyAllWindows()
+
+

eye_face_detection.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Dec 13 00:23:24 2018
+
+@author: amine bahlouli
+"""
+
+#FACE DETECTION
+
+import numpy as np
+import cv2
+
+face_classifier =  cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+
+image = cv2.imread("Trump.jpg")
+gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+
+faces = face_classifier.detectMultiScale(gray,1.3,5)
+
+if faces is ():
+    print("no face found")
+
+for (x,y,w,h) in faces:
+    cv2.rectangle(gray,(x,y),(x+w,y+h),(127,0,255),2)
+    cv2.imshow(",", gray)
+    cv2.waitKey(0)
+
+
+
+#FACE AND EYE DETECTION
+
+eye_classifier = cv2.CascadeClassifier("haarcascade_eye.xml")
+
+for (x,y,w,h) in faces:
+    cv2.rectangle(image,(x,y),(x+w,y+h),(127,0,255),2)
+    cv2.imshow(",", image)
+    cv2.waitKey(0)
+    roi_gray = gray[y:y+h, x:x+w]
+    roi_color = image[y:y+h, x:x+w]
+    eyes = eye_classifier.detectMultiScale(roi_gray)
+    for (ex,ey,ew,eh) in eyes:
+        cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(255,255,0),2)
+        cv2.imshow("img", image)
+        cv2.waitKey(0)
+cv2.destroyAllWindows()
+

eyes_webcam_detection.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Dec 13 01:27:24 2018
+
+@author: amine bahlouli
+"""
+import cv2
+
+
+eye_classifier = cv2.CascadeClassifier("haarcascade_eye.xml")
+face_classifier =  cv2.CascadeClassifier("haarcascade_frontalface_default.xml")
+
+def face_detector(image, size=0.5):
+    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
+    faces = face_classifier.detectMultiScale(gray,1.3,5)
+    if faces is ():
+        return image
+    for (x,y,w,h) in faces:
+        x = x-50
+        w=w+50
+        y =y-50
+        h=h+50
+        cv2.rectangle(image,(x,y),(x+w,y+h),(127,0,255),2)
+        roi_gray = gray[y:y+h, x:x+w]
+        roi_color = image[y:y+h, x:x+w]
+        eyes = eye_classifier.detectMultiScale(roi_gray)
+        for (ex,ey,ew,eh) in eyes:
+            cv2.rectangle(roi_color,(ex,ey),(ex+ew,ey+eh),(255,255,0),2)
+    roi_color =  cv2.flip(roi_color,1)
+    return roi_color
+
+cap = cv2.VideoCapture(0)
+
+while True:
+    ret, frame= cap.read()
+    cv2.imshow("our face extractor ", face_detector(frame))
+    if cv2.waitKey(1)==13:
+        break
+
+cap.release()
+cv2.destroyAllWindows()
+

finding_waldo.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Sat Dec  8 00:46:35 2018
+
+@author: amine bahlouli
+"""
+
+import cv2
+
+import numpy as np
+
+image = cv2.imread("WaldoBeach.jpg",0)
+cv2.imshow("where is waldo3",image)
+cv2.waitKey(0)
+
+
+
+template = cv2.imread("waldo.jpg",0)
+cv2.imshow(",",template)
+cv2.waitKey(0)
+cv2.destroyAllWindows()
+
+result = cv2.matchTemplate(image,template,cv2.TM_CCOEFF)
+min_val,max_val,min_loc,max_loc = cv2.minMaxLoc(result)
+
+#creating the box
+
+top_left = max_loc
+bottom_right = (top_left[0] + 50, top_left[1]+50)
+cv2.rectangle(image,top_left,bottom_right,(0,0,255),5)
+
+cv2.imshow("where is waldo", image)
+cv2.waitKey(0)
+cv2.destroyAllWindows()