From da795509304e44d7c42c0ba91515b2f7eaf6e87a Mon Sep 17 00:00:00 2001 From: Son Nguyen Hoang Date: Sat, 30 Mar 2024 00:18:14 -0400 Subject: [PATCH 1/2] Added Mood & Enhanced Speech Classifications --- Object-Classification/balls.mp4 | Bin 0 -> 1526835 bytes .../object_classification.py | 3 ++ Vehicle-Classification/india.jpg | Bin 0 -> 45922 bytes Vehicle-Classification/vehicle_detection.py | 50 ++++++++++++------ 4 files changed, 36 insertions(+), 17 deletions(-) create mode 100644 Object-Classification/balls.mp4 create mode 100644 Vehicle-Classification/india.jpg diff --git a/Object-Classification/balls.mp4 b/Object-Classification/balls.mp4 new file mode 100644 index 0000000000000000000000000000000000000000..118ecb93d7af2ac2a51f8ff61c2e2536e4c46b64 GIT binary patch literal 1526835 zcmeFWWmFwow;;N4cXubaLvVKs5Zv8@y9bxx8XSVVTX1&>1P>D23GUX#IrsGa`s*IA z-yNghcz=4q+%+0bsRqwzoBMGhqdR5D#qa?cD&8 zjjfxd>6^|hCHdeR04Od1Lm)8F+y8&A|A7G5|3(Y{r{@0(0|%I7R`#aOZ$eame}7ca zhu7EF&#%Y;4hir8cZjMkPDY;qT-W8(UqQkAOHlu8H}vMx@rad!EO^LHw5e!>4v3Sr#V)XE5;No`I4*Tqrm|I3og($vQ3KYW1P ztW3@R!QZa(j2{(5K1!3S1AKc@X(KyR8?!fM1Up+RJ97ZRbhG`-`@hCZY5KQ}(A3H7 zP3LXB$q`+hY)Jpc5ty7^jBNnC&)LQKuh#v|2i5=GT7deWn)6lxAb)8<@&<1U3|>TC zj8x1X*wEr{&j0lJ`U?x-j?BlVA5AvbPC(vw`|I8X*7T|Ks!j%Kxs%KlS=QU7vsazTLF{;QuH30`Y&ikN@uP zpIHB^asRviztjJJ&)dJp^RM(^#ruEd|KHx{|4;QL1RgzqJ<0#2ud)E10Kkm1smt4UJg6uoB|ZJW1}Xok z|1a2^qyO;ppV&W~x9ea3DR1kq4gbRaqW*)G{a=;GhG1fpsLXc0g+8;CO88A)kRgf&4M_0ec{C zL7=8YK%S`Y0XZKC#Ig$bTLj_>0b&Q_Xuw{`5Wv1~d9(t0fIcb^KQAz{#6W#fw*eh9 zAdV6s4b81j1_paB~1~fI6oEer|x86aqbP1cA_J09~N3LVeb0}zNB$OG901fm52JV`*W8yGo2hEfjnc^w3D1pMO|1APLH zk9hfj?gSu%2Wqhd%n>m_>jH5c0CfTYy9~hZ07wOanB#%ErvSJrkmDi<#01nB8v($Z z0UjEVvnfCWwupc)#4uovZh;=`0&@z?B?7QF`y}8W*aImN07_s#pbxmf{z#Po_yO`5 zfCgsb%@@)X&`&C$7GHom0=c5`0&+b-P6xzi4fq3Ql^&QgqPJS?1Mvd@0`3Q8{_D*J`tQ_2=POL1X@7YW_*^P_=wlotUW>!#=kYeN@eLFXs*qfS}00J?4 z2TvO_a~D!J78Z6!HWpScfNAOC;=s$y?C$Q)^!8F`guiu=29-vXC0t8QFL`n+dRZu=BEbu(Gm~ z+L{Sint6~qyBY)7dr}8yPrwxTG;lH%U}a(fjDQbPTPqJUQ-e2;tbm1qlaZZ;nE()) ziKP?p0%!o3vXZ(unc3J_IRhBC2e+w-3xJq7+6u4$AsCr@+1r^3u(2|*vXYt`IlCA* zIDfKoc+>dXfunx$>6JP;2dQWO&Z~w{25}*wJVX~1r+gOY*YZDVITO*gZK&0?^JbRxT!%M#eU8nkGQO zfHy=dJHQ`cK&K z-OS3u(#05%**loo8Ccjm0Mfsi4uI(=Gf%*`0Pq@SWoPAL_8+tX@XjW{#zE?AVrFM% z;_4#6!SWWklhIq1oy?pq0Uu5#2LHVPZ*nIS0XE=$$C=dD7%28z0>)N=#aq&xK$G4u z7kitxwvjr%B`m*~QF3fP>V;!N%jQJ_2k&lSv)_LRpwN zN!^Uy3;^A~J#%rg{KYVKHFvi10=D7?wgSe;z!I1cJD{zk=2kYqizpDUl>;ytz!U-z z0MQyb0z>yULLh$-NTbgzJQ(z00e8!1ILd)+kfizh+&1o7SjQJVMO-xHtxr(B>0jBP zZD22g=!0br;CaScGAuLX?sw&;nPc}-?Hr4?jtw2(t)|Xz>5m~QP@Nc$S#>$2=FKA+ zl=(mS|FShjLpB`%PlRlqsGaHXIFXK&P7y6JQ*s<5{s_uPcV8uD!pQpZ;dB1t{W^?q zE7PiU`9ZHmaP56ko-K#A>adG|W7KO+K+>dVz`SiSCZo#B9MZ->``wokfyNHu%BvX? zHoJV9>Mv_zP1`)vm~wQnD{&E289IeKta=CEFLm3yS>xqNr+>_axAn%*;zjWPbLu=Z#{VuhAyl@5W9U=Tk5<#{4yBU^>7 z^$XiWtAaQETy6t;dPdmpyfD~i;Upn;x{VQI40KHZo9zX|zrXow{88Xh0?0OOv^>coCI4X->`oJWy*Yk=~c#&O!rQc%+?|-VZmPd=w8l2pwN8 zEqtthPqefQvg7=c2GO0uETtpB{XW$-rX^WY%U)#gjYgI&=C~QoaKPy0|5vD@2xRNC%+BhXeM}V#}f<)eR1f!Scnuv z=(rMrZ|rP&NW2q+AAf|1kaoVh}c=NZ!?zR~C3brAS%IWp6vS1n;`}j7fM&Nefs8D ztUfD%-E!SHU0R{5!%5d$*~vP!}{S4pGr&&li@{=O*Lgh?)tDez&P(39X#~CB6F4wmWSRZ-L+IP zXqSX$V-F6QPhB&fmIRcqJX1+Q(ZbmmSw&J+?J52ng-2>il~25H&8Rw?Z18{<*$65u zlX#F&_^38^f0}AbzLYTEKN(HuDtst)J?3lhpECBVPU=XgxRsT zck1JDf>(e>;wAG>11KSVDa6j9bn89VJJan9G*2U&VRLB`0iU>)0W9o)hVglHLEA)ef0mb{&u~i!TTG%EcChBkMOE+$KOtCt;yhDDBAAg{E47JQg zcz+mp=JhOZIR}na#nrF&JceqUu)+lpsLm}h^}ZgAHP1ISJFBfT>T{5kM96+5-w^iX zC7a!XK1k_U=0pVP74ziZ@;llL$T_#NfWCbEv^p5eMHWk#6-wG7C#gC^dUo3>V#gV3 z0*<)7Kk`14lxtt~rh8)j{m=>)RssfJ-=IBvw*}__=Z}aT)d6*mM1u#~l1C3dFBsOG z6Rs7yxldinA3o*%V4h00Z(#g6VXp#d+31YhZC2xR>~!Is8zcALDnxd(foo#k$=#CU zR5w{%P}R>n2D@v?Zcu{%(!XKf^m-WPRcxx^nyxW7v`Rm0{5t#IrWQW`uBRTkXQFZ{ z6B=E0M})`77?oImzooy=npJ7kJtu>TKk>+?sZ8{9C>%9x|IWaZP3e!^jK$ z5+iDdi8jIJg}YF}DFyznsbn4!s71(M?Mg_IW|tby$L}DqH)xavPu>x5Qy9y7Jp0=a z(tfH+yWP( ziNOk$PZFzLPLlKKiu%?1wFs+&@B<>(SH-(l-%gZT9yAXoH(?%QjbV%HLnal;0^3f6 zI}T`5%>wP53H#j|TsIL71{9d@{DU(E6RDLM{gTP4uL?gISq+iFBj$z_`ap;aSg^g# zFsT&p>0b!nIka`;%~$#TEL0}7IGMi_bY{?GDbY-7^W>i3n9x9s7wQtPEi(%n#n^}L zj5~?ZhE+vGLN&l1gl-S)ANJ%^Pu~7MP>$L>RgOoxJR9t<_~sM zYQ+FsN7uJVx&fl**m=@63iL*bgp77neMg!cPa^JuW)WS}B-m@yO?zaXSErCO5zN;{ z>3TG%XU(zL!ojLjaS9gc&5rI<6_(E3pXKYYk;VXjtCv-dHQbF-znJckJ++jIYeR;yn+G
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a/Object-Classification/object_classification.py b/Object-Classification/object_classification.py index c1ca765..46961db 100644 --- a/Object-Classification/object_classification.py +++ b/Object-Classification/object_classification.py @@ -81,6 +81,9 @@ def main(): choice = input("Choose 'image', 'video', or 'webcam': ").lower() if choice == 'image': image_path = input("Enter the image path: ") + + print("Check the popup window for the results.") + results, image = classify_image(model, image_path) annotated_image = annotate_image(image, results) annotated_image.show() diff --git a/Vehicle-Classification/india.jpg b/Vehicle-Classification/india.jpg new file mode 100644 index 0000000000000000000000000000000000000000..701e477d8e57274fc3d3a0ffb772de76a6ccc2e0 GIT binary patch literal 45922 zcmbT7WmFtN*XIY<;F{nwK+xc>A$Wj5@WFz+4NmYtfZzlO4#8z`7~I`m1_@zscMGyS z@9uf`oc*-B^`)iiRQK<6Raf2nzxR3Zc^yEYB&R3`Kte(S{8xGY3*eOXvatjJR8#=0 zFSjrO$Wj0R%F7uNKmq`eLV7v==lp+_=VgEl02>1X3j-Y+3lj?m2OAfkk^mnM51)pN 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Replace `'model_path'` with the actual paths to your models.\n", + "\n", + "#### Object Detection (YOLO)\n", + "```python\n", + "import cv2\n", + "yolo_model = cv2.dnn.readNetFromDarknet('yolov3.cfg', 'yolov3.weights')\n", + "```\n", + "\n", + "#### Face Detection (TensorFlow)\n", + "```python\n", + "import tensorflow as tf\n", + "tf_model = tf.keras.models.load_model('model_path')\n", + "```\n", + "\n", + "#### Mood Classification (PyTorch)\n", + "```python\n", + "import torch\n", + "torch_model = torch.load('model_path')\n", + "```\n", + "\n", + "### Running Classifications\n", + "Demonstrate how to use these models to classify images.\n", + "\n", + "#### Object Detection with YOLO\n", + "```python\n", + "def detect_objects_yolo(image_path, model):\n", + " # Image loading and processing for YOLO\n", + " return image\n", + "\n", + "yolo_result = detect_objects_yolo('path/to/object/image.jpg', yolo_model)\n", + "```\n", + "\n", + "#### Face Detection with TensorFlow\n", + "```python\n", + "def detect_faces_tf(image_path, model):\n", + " # Image loading and processing for TensorFlow\n", + " return predictions\n", + "\n", + "tf_result = detect_faces_tf('path/to/face/image.jpg', tf_model)\n", + "```\n", + "\n", + "#### Mood Classification with PyTorch\n", + "```python\n", + "def classify_mood_torch(image_path, model):\n", + " # Image loading and processing for PyTorch\n", + " return predictions\n", + "\n", + "torch_result = classify_mood_torch('path/to/mood/image.jpg', torch_model)\n", + "```\n", + "\n", + "### Results and Analysis\n", + "Discuss and display the results.\n", + "\n", + "#### YOLO Object Detection\n", + "```python\n", + "# Displaying YOLO results\n", + "```\n", + "\n", + "#### TensorFlow Face Detection\n", + "```python\n", + "# Displaying TensorFlow results\n", + "```\n", + "\n", + "#### PyTorch Mood Classification\n", + "```python\n", + "# Displaying PyTorch results\n", + "```\n", + "\n", + "### More Classifiers\n", + "For each additional classifier mentioned in the README, add similar sections as above. For example, Character Recognition (OCR), Animal Classification, etc.\n", + "\n", + "#### Character Recognition (OCR)\n", + "```python\n", + "# Load OCR model, demonstrate character recognition\n", + "```\n", + "\n", + "#### Animal Classification\n", + "```python\n", + "# Load animal classification model, demonstrate usage\n", + "```\n", + "\n", + "### Speech Recognition\n", + "```python\n", + "# Discuss the speech recognition module, demonstrate usage\n", + "```\n", + "\n", + "## Conclusion\n", + "Summarize the capabilities and findings of using these classifiers.\n", + "\n", + "## Future Work\n", + "- Enhance the models and expand the notebook with more detailed examples and analyses.\n", + "- Incorporate feedback and new classifiers as the project evolves.\n" + ], + "metadata": { + "collapsed": false + }, + "id": "e6141e55235e57c3" + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + 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f84a61b..fc9b152 100644 --- a/Animals-Classification/animal_classification.py +++ b/Animals-Classification/animal_classification.py @@ -17,20 +17,26 @@ def classify_image(model, image): image_array = preprocess_input(image_array) predictions = model.predict(image_array) - decoded_predictions = decode_predictions(predictions, top=1)[0] + decoded_predictions = decode_predictions(predictions, top=5)[0] return decoded_predictions def annotate_image(image, predictions): draw = ImageDraw.Draw(image) - font = ImageFont.load_default() + font_size = 20 # Increase font size + font = ImageFont.truetype("arial.ttf", font_size) text_y = 10 for i, (id, label, prob) in enumerate(predictions): text = f"{label} ({prob * 100:.2f}%)" draw.text((10, text_y), text, fill="red", font=font) - text_y += 20 + text_y += font_size + 10 + + # Draw rectangle for the object + # Since we're using MobileNetV2 without specific object localization, we draw a generic box + draw.rectangle([10, text_y, 200, text_y + font_size], outline="red", width=2) + text_y += font_size + 10 # Update y coordinate for text return image @@ -56,7 +62,7 @@ def process_input(source, model): key = cv2.waitKey(1) & 0xFF if key in [27, ord('q')]: # ESC or Q key to exit break - if cv2.getWindowProperty("Image", cv2.WND_PROP_VISIBLE) < 1: # Check if the window is closed + if cv2.getWindowProperty("Image", cv2.WND_PROP_VISIBLE) < 1: break cap.release() @@ -71,10 +77,14 @@ def process_input(source, model): choice = input("Enter 'image', 'video', or 'webcam': ").lower() if choice == 'image': image_path = input("Enter the image path: ") + print("Check the popup window for detailed results.") image = Image.open(image_path) predictions = classify_image(model, image) annotated_image = annotate_image(image, predictions) annotated_image.show() elif choice in ['video', 'webcam']: source = 0 if choice == 'webcam' else input("Enter the video path: ") + print("Check the popup window for detailed results.") process_input(source, model) + else: + print("Invalid choice. 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