Task2

check.py

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+# import cv2
+# (width, height) = (130, 100)
+# cap = cv2.VideoCapture(0)
+# while (cap.isOpened()):
+#     ret, img = cap.read()
+#     img=cv2.flip(img, 1)
+#     cv2.rectangle(img, (20, 20), (250, 250), (255, 0, 0), 3)
+#     cv2.imshow("RGB Output", img)
+#     img1 = img[20:250,20:250]
+#     imCopy = img1.copy()
+#     gray = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)
+#     blur = cv2.GaussianBlur(gray, (5, 5), 0)
+#     ret, thresh1 = cv2.threshold(blur, 10, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+#     hand_resize = cv2.resize(thresh1, (width, height))
+#     cv2.imshow("Threshold", thresh1)
+#     contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+#     cv2.drawContours(imCopy, contours, -1, (0, 255, 0))
+#     cv2.imshow('Draw Contours', imCopy)
+
+#     k = 0xFF & cv2.waitKey(10)
+#     if k == 27:
+#         break
+
+# cap.release()
+# cv2.destroyAllWindows()
+
+# STATIC IMAGES INPUT
+
+import cv2
+img = cv2.imread('img.jpg')
+cv2.imshow("Original Image", img)
+img1 = cv2.resize(img, (300, 300))
+cv2.imshow("Resized image", img1)
+# ret, img = cap.read()
+img2=cv2.flip(img1, 1)
+cv2.imshow("Flipped image", img2)
+cv2.rectangle(img2, (20, 20), (250, 250), (255, 0, 0), 3)
+cv2.imshow("RGB Output", img2)
+img3 = img2[20:250,20:250]
+imCopy = img3.copy()
+gray = cv2.cvtColor(img3, cv2.COLOR_BGR2GRAY)
+cv2.imshow("grayscale",gray)
+blur = cv2.GaussianBlur(gray, (5, 5), 0)
+cv2.imshow("GaussianBlur",blur)
+ret, thresh1 = cv2.threshold(blur, 10, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+hand_resize = cv2.resize(thresh1, (500, 400))
+cv2.imshow("Threshold", thresh1)
+contours, hierarchy = cv2.findContours(thresh1, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)
+cv2.drawContours(imCopy, contours, -1, (0, 0, 0))
+cv2.imshow('Draw Contours', imCopy)
+# cv2.imshow("Image", img2)
+interrupt = cv2.waitKey(0) & 0xFF
+if interrupt == 27:
+    cv2.destroyAllWindows()

collect-data.py

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+import cv2
+import os
+
+if not os.path.exists("data"):
+    os.makedirs("data")
+    os.makedirs("data/train")
+    os.makedirs("data/train/0")
+    os.makedirs("data/train/1")
+    os.makedirs("data/train/2")
+    os.makedirs("data/train/3")
+    os.makedirs("data/train/4")
+    os.makedirs("data/train/5")
+
+
+mode = 'train'
+directory = 'data/'+mode+'/'
+
+# url = '<YOUR IP ADDRESS>/video'
+# cap=cv2.VideoCapture(url)
+
+cap=cv2.VideoCapture(0)
+
+while True:
+    _, frame = cap.read()
+    frame = cv2.flip(frame, 1)
+
+    cv2.putText(frame, "Sayak-Rana", (160, 240), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 3)
+
+    count = {'zero': len(os.listdir(directory+"/0")),
+             'one': len(os.listdir(directory+"/1")),
+             'two': len(os.listdir(directory+"/2")),
+             'three': len(os.listdir(directory+"/3")),
+             'four': len(os.listdir(directory+"/4")),
+             'five': len(os.listdir(directory+"/5"))}
+
+    cv2.putText(frame, "MODE : "+mode, (10, 50), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 1)
+    cv2.putText(frame, "IMAGE COUNT", (10, 90), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 1)
+    cv2.putText(frame, "ZERO : "+str(count['zero']), (10, 115), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 1)
+    cv2.putText(frame, "ONE : "+str(count['one']), (10, 140), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 1)
+    cv2.putText(frame, "TWO : "+str(count['two']), (10, 165), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 1)
+    cv2.putText(frame, "THREE : "+str(count['three']), (10, 190), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 1)
+    cv2.putText(frame, "FOUR : "+str(count['four']), (10, 215), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 1)
+    cv2.putText(frame, "FIVE : "+str(count['five']), (10, 240), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,0,0), 1)
+
+
+    x1 = int(0.5*frame.shape[1])
+    y1 = 10
+    x2 = frame.shape[1]-10
+    y2 = int(0.5*frame.shape[1])
+    cv2.rectangle(frame, (x1-1, y1-1), (x2+1, y2+1), (255,0,0) ,3)
+    roi = frame[y1:y2, x1:x2]
+    roi = cv2.resize(roi, (200, 200)) 
+    cv2.putText(frame, "R.O.I", (440, 350), cv2.FONT_HERSHEY_COMPLEX_SMALL, 1, (0,225,0), 3)
+    cv2.imshow("Frame", frame)
+
+    roi = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)
+    _, roi = cv2.threshold(roi, 120, 255, cv2.THRESH_OTSU)
+    cv2.imshow("ROI", roi)
+
+
+
+    interrupt = cv2.waitKey(10)
+    if interrupt & 0xFF == 27:
+        break
+    if interrupt & 0xFF == ord('0'):
+        cv2.imwrite(directory+'0/'+str(count['zero'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('1'):
+        cv2.imwrite(directory+'1/'+str(count['one'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('2'):
+        cv2.imwrite(directory+'2/'+str(count['two'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('3'):
+        cv2.imwrite(directory+'3/'+str(count['three'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('4'):
+        cv2.imwrite(directory+'4/'+str(count['four'])+'.jpg', roi)
+    if interrupt & 0xFF == ord('5'):
+        cv2.imwrite(directory+'5/'+str(count['five'])+'.jpg', roi)
+
+cap.release()
+cv2.destroyAllWindows()

defects.py

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+import cv2
+import numpy as np
+import math
+
+cap=cv2.VideoCapture(0)
+
+while(cap.isOpened()):
+    ret, img = cap.read()
+    img=cv2.flip(img, 1)
+    cv2.rectangle(img,(20,20),(250,250),(255,0,0),3)
+    crop_img = img[20:250, 20:250]
+    grey = cv2.cvtColor(crop_img, cv2.COLOR_BGR2GRAY)
+    value = (35, 35)
+    blurred = cv2.GaussianBlur(grey, value, 0)
+    _, thresh1 = cv2.threshold(blurred, 127, 255, cv2.THRESH_BINARY_INV+cv2.THRESH_OTSU)
+    contours, hierarchy = cv2.findContours(thresh1.copy(),cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)
+    cnt = max(contours, key = lambda x: cv2.contourArea(x))
+    x,y,w,h = cv2.boundingRect(cnt)
+    cv2.rectangle(crop_img,(x,y),(x+w,y+h),(0,0,255),0)
+
+    hull = cv2.convexHull(cnt)
+    drawing = np.zeros(crop_img.shape,np.uint8)
+
+    cv2.drawContours(drawing,[cnt],0,(0,255,0),0)
+    cv2.drawContours(drawing,[hull],0,(0,0,255),0)
+
+    hull = cv2.convexHull(cnt,returnPoints = False)
+    defects = cv2.convexityDefects(cnt,hull)
+
+    count_defects = 0
+    cv2.drawContours(thresh1, contours, -1, (0,255,0), 3)
+
+    for i in range(defects.shape[0]):
+        s,e,f,d = defects[i,0]
+        start = tuple(cnt[s][0])
+        end = tuple(cnt[e][0])
+        far = tuple(cnt[f][0])
+        a = math.sqrt((end[0] - start[0])**2 + (end[1] - start[1])**2)
+        b = math.sqrt((far[0] - start[0])**2 + (far[1] - start[1])**2)
+        c = math.sqrt((end[0] - far[0])**2 + (end[1] - far[1])**2)
+        angle = math.acos((b**2 + c**2 - a**2)/(2*b*c)) * 57
+
+        if angle <= 90:
+            count_defects += 1
+            cv2.circle(crop_img,far,1,[0,0,255],-1)
+
+        cv2.line(crop_img,start,end,[0,255,0],2)
+
+    if count_defects == 1:
+        cv2.putText(img,"Number : 2", (50,450), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 1)
+    elif count_defects == 2:
+        cv2.putText(img, "Number : 3", (50,450), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 1)
+    elif count_defects == 3:
+        cv2.putText(img,"Number : 4", (50,450), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 1)
+    elif count_defects == 4:
+        cv2.putText(img,"Number : 5", (50,450), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 1)
+    elif count_defects == 5:
+        cv2.putText(img,"Number : 6", (50,450), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 1)
+    else:
+        cv2.putText(img,"Number : 1", (50,450), cv2.FONT_HERSHEY_SIMPLEX, 1, (255,255,255), 1)
+
+    cv2.imshow('Gesture', img)
+    cv2.imshow('Contours', drawing)
+    cv2.imshow('Defects', crop_img)
+    cv2.imshow('Binary Image', thresh1)
+
+    k = cv2.waitKey(10)
+    if k == 27:
+        break

requirements.txt

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+absl-py==0.14.1
+astunparse==1.6.3
+cachetools==4.2.4
+certifi==2021.5.30
+charset-normalizer==2.0.6
+clang==5.0
+flatbuffers==1.12
+gast==0.4.0
+google-auth==1.35.0
+google-auth-oauthlib==0.4.6
+google-pasta==0.2.0
+grpcio==1.41.0
+h5py==3.1.0
+idna==3.2
+keras==2.6.0
+Keras-Preprocessing==1.1.2
+Markdown==3.3.4
+numpy==1.19.5
+oauthlib==3.1.1
+opencv-python==4.5.3.56
+opt-einsum==3.3.0
+Pillow==8.4.0
+protobuf==3.18.0
+pyasn1==0.4.8
+pyasn1-modules==0.2.8
+requests==2.26.0
+requests-oauthlib==1.3.0
+rsa==4.7.2
+scipy==1.7.1
+six==1.15.0
+tensorboard==2.6.0
+tensorboard-data-server==0.6.1
+tensorboard-plugin-wit==1.8.0
+tensorflow==2.6.0
+tensorflow-estimator==2.6.0
+termcolor==1.1.0
+typing-extensions==3.7.4.3
+urllib3==1.26.7
+Werkzeug==2.0.1
+wrapt==1.12.1